diff --git a/analysis_pipelines.ts b/analysis_pipelines.ts
deleted file mode 100644
index a35ee7a..0000000
--- a/analysis_pipelines.ts
+++ /dev/null
@@ -1,133 +0,0 @@
-// analysis_pipelines.ts - High-level workflows for common analysis tasks.
-
-import { SignalProcessor } from './signal_processing_convolution';
-import { TimeSeriesAnalyzer, STLDecomposition } from './timeseries';
-
-/**
- * The comprehensive result of a denoise and detrend operation.
- */
-export interface DenoiseAndDetrendResult {
-  original: number[];
-  smoothed: number[];
-  decomposition: STLDecomposition;
-}
-
-/**
- * The result of an automatic SARIMA parameter search.
- */
-export interface AutoArimaResult {
-  bestModel: {
-    p: number;
-    d: number;
-    q: number;
-    P: number;
-    D: number;
-    Q: number;
-    s: number; // Correctly included
-    aic: number;
-  };
-  searchLog: { p: number; d: number; q: number; P: number; D: number; Q: number; s: number; aic: number }[];
-}
-
-
-/**
- * A class containing high-level analysis pipelines that combine
- * functions from various processing libraries.
- */
-export class AnalysisPipelines {
-
-  /**
-   * A full pipeline to take a raw signal, smooth it to remove noise,
-   * and then decompose it into trend, seasonal, and residual components.
-   * @param series The original time series data.
-   * @param period The seasonal period for STL decomposition.
-   * @param smoothWindow The window size for the initial smoothing (denoising) pass.
-   * @returns An object containing the original, smoothed, and decomposed series.
-   */
-  static denoiseAndDetrend(series: number[], period: number, smoothWindow: number = 5): DenoiseAndDetrendResult {
-    // Ensure window is odd for symmetry
-    if (smoothWindow > 1 && smoothWindow % 2 === 0) {
-        smoothWindow++;
-    }
-    const smoothed = SignalProcessor.smooth(series, {
-      method: 'gaussian',
-      windowSize: smoothWindow
-    });
-
-    const decomposition = TimeSeriesAnalyzer.stlDecomposition(smoothed, period);
-
-    return {
-      original: series,
-      smoothed: smoothed,
-      decomposition: decomposition,
-    };
-  }
-
-  /**
-   * [FINAL CORRECTED VERSION] Performs a full grid search to find the optimal SARIMA parameters.
-   * This version now correctly includes 's' in the final result object.
-   * @param series The original time series data.
-   * @param seasonalPeriod The seasonal period of the data (e.g., 7 for weekly, 12 for monthly).
-   * @returns An object containing the best model parameters and a log of the search.
-   */
-  static findBestArimaParameters(
-    series: number[], 
-    seasonalPeriod: number,
-    maxD: number = 1, 
-    maxP: number = 2, 
-    maxQ: number = 2,
-    maxSeasonalD: number = 1,
-    maxSeasonalP: number = 2,
-    maxSeasonalQ: number = 2
-  ): AutoArimaResult {
-
-    const searchLog: any[] = [];
-    let bestModel: any = { aic: Infinity };
-
-    const calculateAIC = (residuals: number[], numParams: number): number => {
-      const n = residuals.length;
-      if (n === 0) return Infinity;
-      const sse = residuals.reduce((sum, r) => sum + r * r, 0);
-      if (sse < 1e-9) return -Infinity; // Perfect fit
-      const logLikelihood = -n / 2 * (Math.log(2 * Math.PI) + Math.log(sse / n)) - n / 2;
-      return 2 * numParams - 2 * logLikelihood;
-    };
-    
-    // Grid search over all parameter combinations
-    for (let d = 0; d <= maxD; d++) {
-    for (let p = 0; p <= maxP; p++) {
-    for (let q = 0; q <= maxQ; q++) {
-    for (let D = 0; D <= maxSeasonalD; D++) {
-    for (let P = 0; P <= maxSeasonalP; P++) {
-    for (let Q = 0; Q <= maxSeasonalQ; Q++) {
-        // Skip trivial models where nothing is done
-        if (p === 0 && d === 0 && q === 0 && P === 0 && D === 0 && Q === 0) continue;
-
-        const options = { p, d, q, P, D, Q, s: seasonalPeriod };
-        try {
-            const { residuals } = TimeSeriesAnalyzer.arimaForecast(series, options, 0);
-            const numParams = p + q + P + Q;
-            const aic = calculateAIC(residuals, numParams);
-
-            // Construct the full model info object, ensuring 's' is included
-            const modelInfo = { p, d, q, P, D, Q, s: seasonalPeriod, aic };
-            searchLog.push(modelInfo);
-
-            if (modelInfo.aic < bestModel.aic) {
-              bestModel = modelInfo;
-            }
-        } catch (error) {
-            // Skip invalid parameter combinations that cause errors
-        }
-    } } } } } }
-
-    if (bestModel.aic === Infinity) {
-        throw new Error("Could not find a suitable SARIMA model. The data may be too short or complex.");
-    }
-    
-    // Sort the log by AIC for easier reading
-    searchLog.sort((a, b) => a.aic - b.aic);
-    
-    return { bestModel, searchLog };
-  }
-}
diff --git a/api-documentation.html b/api-documentation.html
index 67e690a..6da21b0 100644
--- a/api-documentation.html
+++ b/api-documentation.html
@@ -28,7 +28,7 @@
     <script>
         window.onload = function() {
             const ui = SwaggerUIBundle({
-                spec: {"openapi":"3.0.0","info":{"title":"My Express API","version":"1.0.0","description":"API documentation for my awesome Express app"},"servers":[{"url":"http://localhost:3000"}],"paths":{"/api/health":{"get":{"summary":"Health check endpoint","description":"Returns the health status of the API","tags":["Health"],"responses":{"200":{"description":"API is healthy","content":{"application/json":{"schema":{"type":"object","properties":{"status":{"type":"string","example":"OK"},"timestamp":{"type":"string","format":"date-time"}}}}}}}}},"/api/unique":{"post":{"summary":"Get unique values from a data series","description":"Returns an array of unique values from the provided data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"}}}}}},"responses":{"200":{"description":"Unique values calculated successfully"},"400":{"description":"Invalid input data"}}}},"/api/mean":{"post":{"summary":"Calculate mean of a data series","description":"Returns the arithmetic mean of the provided data series, optionally filtered by conditions","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Mean calculated successfully"},"400":{"description":"Invalid input data"}}}},"/api/count":{"post":{"summary":"Count data points in a series","description":"Returns the count of data points in the series, optionally filtered by conditions","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Count calculated successfully"},"400":{"description":"Invalid input data"}}}},"/api/variance":{"post":{"summary":"Calculate variance of a data series","description":"Returns the variance of the provided data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Variance calculated successfully"},"400":{"description":"Invalid input data"}}}},"/api/std":{"post":{"summary":"Calculate standard deviation of a data series","description":"Returns the standard deviation of the provided data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Standard deviation calculated successfully"},"400":{"description":"Invalid input data"}}}},"/api/percentile":{"post":{"summary":"Calculate percentile of a data series","description":"Returns the specified percentile of the provided data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"percent":{"type":"number","description":"Percentile to calculate (0-100)","example":95},"ascending":{"type":"boolean","description":"Sort order","default":true},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Percentile calculated successfully"},"400":{"description":"Invalid input data"}}}},"/api/median":{"post":{"summary":"Calculate median of a data series","description":"Returns the median (50th percentile) of the provided data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Median calculated successfully"},"400":{"description":"Invalid input data"}}}},"/api/mode":{"post":{"summary":"Calculate mode of a data series","description":"Returns the mode (most frequent values) of the provided data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Mode calculated successfully"},"400":{"description":"Invalid input data"}}}},"/api/max":{"post":{"summary":"Find maximum value in a data series","description":"Returns the maximum value from the provided data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Maximum value found successfully"},"400":{"description":"Invalid input data"}}}},"/api/min":{"post":{"summary":"Find minimum value in a data series","description":"Returns the minimum value from the provided data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Minimum value found successfully"},"400":{"description":"Invalid input data"}}}},"/api/correlation":{"post":{"summary":"Calculate correlation between two data series","description":"Returns the Pearson correlation coefficient between two data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series1":{"$ref":"#/components/schemas/DataSeries"},"series2":{"$ref":"#/components/schemas/DataSeries"}}}}}},"responses":{"200":{"description":"Correlation calculated successfully"},"400":{"description":"Invalid input data or series have different lengths"}}}},"/api/series/moving-average":{"post":{"summary":"Calculate moving average of a data series","description":"Returns the moving average of the provided data series with specified window size","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"windowSize":{"type":"integer","description":"Size of the moving window","minimum":1,"example":5}}}}}},"responses":{"200":{"description":"Moving average calculated successfully"},"400":{"description":"Invalid input data or window size"}}}},"/api/series/rolling":{"post":{"summary":"Get rolling windows of a data series","description":"Returns rolling windows of the provided data series with specified window size","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"windowSize":{"type":"integer","description":"Size of the rolling window","minimum":1,"example":3}}}}}},"responses":{"200":{"description":"Rolling windows calculated successfully"},"400":{"description":"Invalid input data or window size"}}}},"/api/series/auto-arima-find":{"post":{"summary":"(EXPERIMENTAL) Automatically find best SARIMA parameters","description":"Performs a grid search to find the best SARIMA parameters based on AIC. NOTE - This is a simplified estimation and may not find the true optimal model. For best results, use the identification tools and the 'manual-forecast' endpoint.","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"seasonalPeriod":{"type":"integer","description":"The seasonal period of the data (e.g., 7 for weekly).","example":7}}}}}},"responses":{"200":{"description":"The best model found and the search log."},"400":{"description":"Invalid input data."}}}},"/api/series/manual-forecast":{"post":{"summary":"Generate a forecast with manually specified SARIMA parameters","description":"This is the primary forecasting tool. It allows an expert user (who has analyzed ACF/PACF plots) to apply a specific SARIMA model to a time series and generate a forecast.","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"options":{"$ref":"#/components/schemas/ARIMAOptions"},"forecastSteps":{"type":"integer","description":"The number of future time steps to predict.","example":7}}}}}},"responses":{"200":{"description":"The forecast results."},"400":{"description":"Invalid input data"}}}},"/api/series/identify-correlations":{"post":{"summary":"Calculate ACF and PACF for a time series","description":"Returns the Autocorrelation and Partial Autocorrelation function values, which are essential for identifying SARIMA model parameters.","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"maxLag":{"type":"integer","description":"The maximum number of lags to calculate.","example":40}}}}}}},"responses":{"200":{"description":"The calculated ACF and PACF values."},"400":{"description":"Invalid input data."}}},"/api/series/decompose-stl":{"post":{"summary":"Decompose a time series into components","description":"Applies Seasonal-Trend-Loess (STL) decomposition to separate the series into trend, seasonal, and residual components.","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"period":{"type":"integer","description":"The seasonal period of the data (e.g., 7 for weekly).","example":7}}}}}}},"responses":{"200":{"description":"The decomposed components of the time series."},"400":{"description":"Invalid input data."}}},"/api/ml/kmeans":{"post":{"summary":"Perform K-means clustering","description":"Performs K-means clustering on the provided data matrix","tags":["Machine Learning"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"matrix":{"$ref":"#/components/schemas/DataMatrix"},"nClusters":{"type":"integer","description":"Number of clusters","minimum":1,"example":3},"options":{"type":"object","description":"K-means options"}}}}}},"responses":{"200":{"description":"K-means clustering completed successfully"},"400":{"description":"Invalid input data"}}}},"/api/time/week-number":{"post":{"summary":"Get week number from date","description":"Returns the ISO week number for the provided date string","tags":["Time"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"date":{"type":"string","format":"date","description":"Date string in ISO format","example":"2024-03-15"}}}}}},"responses":{"200":{"description":"Week number calculated successfully"},"400":{"description":"Invalid date format"}}}},"/api/time/same-day-last-year":{"post":{"summary":"Get same day of week from last year","description":"Returns the date string for the same day of the week from the previous year","tags":["Time"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"date":{"type":"string","format":"date","description":"Date string in ISO format","example":"2024-03-15"}}}}}},"responses":{"200":{"description":"Same day last year calculated successfully"},"400":{"description":"Invalid date format"}}}},"/api/retail/purchase-rate":{"post":{"summary":"Calculate purchase rate","description":"Calculates the purchase rate as a percentage of product purchases over total transactions","tags":["Retail"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"productPurchases":{"type":"number","description":"Number of product purchases","example":150},"totalTransactions":{"type":"number","description":"Total number of transactions","example":1000}}}}}},"responses":{"200":{"description":"Purchase rate calculated successfully"},"400":{"description":"Invalid input data or division by zero"}}}},"/api/retail/lift-value":{"post":{"summary":"Calculate lift value","description":"Calculates the lift value for market basket analysis","tags":["Retail"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"jointPurchaseRate":{"type":"number","description":"Joint purchase rate of both products","example":0.05},"productAPurchaseRate":{"type":"number","description":"Purchase rate of product A","example":0.2},"productBPurchaseRate":{"type":"number","description":"Purchase rate of product B","example":0.3}}}}}},"responses":{"200":{"description":"Lift value calculated successfully"},"400":{"description":"Invalid input data or division by zero"}}}},"/api/retail/cost-ratio":{"post":{"summary":"Calculate cost ratio","description":"Calculates the cost ratio (cost divided by sale price)","tags":["Retail"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"cost":{"type":"number","description":"Cost of the product","example":50},"salePrice":{"type":"number","description":"Sale price of the product","example":100}}}}}},"responses":{"200":{"description":"Cost ratio calculated successfully"},"400":{"description":"Invalid input data or division by zero"}}}},"/api/retail/gross-margin":{"post":{"summary":"Calculate gross margin rate","description":"Calculates the gross margin rate as a percentage","tags":["Retail"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"salePrice":{"type":"number","description":"Sale price of the product","example":100},"cost":{"type":"number","description":"Cost of the product","example":60}}}}}},"responses":{"200":{"description":"Gross margin rate calculated successfully"},"400":{"description":"Invalid input data or division by zero"}}}},"/api/retail/average-spend":{"post":{"summary":"Calculate average spend per customer","description":"Calculates the average amount spent per customer","tags":["Retail"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"totalRevenue":{"type":"number","description":"Total revenue","example":50000},"numberOfCustomers":{"type":"number","description":"Number of customers","example":500}}}}}},"responses":{"200":{"description":"Average spend calculated successfully"},"400":{"description":"Invalid input data or division by zero"}}}},"/api/retail/purchase-index":{"post":{"summary":"Calculate purchase index","description":"Calculates the purchase index (items per 1000 customers)","tags":["Retail"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"totalItemsSold":{"type":"number","description":"Total number of items sold","example":2500},"numberOfCustomers":{"type":"number","description":"Number of customers","example":1000}}}}}},"responses":{"200":{"description":"Purchase index calculated successfully"},"400":{"description":"Invalid input data or division by zero"}}}},"/api/predict/forecast":{"post":{"summary":"Generate time series forecast","description":"Generates a forecast for time series data using linear regression","tags":["Prediction"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"forecastPeriods":{"type":"integer","description":"Number of periods to forecast","minimum":1,"example":10}}}}}},"responses":{"200":{"description":"Forecast generated successfully"},"400":{"description":"Invalid input data"}}}},"/api/series/smooth":{"post":{"summary":"Smooth a 1D data series","description":"Applies a smoothing filter (Gaussian or Moving Average) to a 1D data series to reduce noise.","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"options":{"$ref":"#/components/schemas/SmoothingOptions"}}}}}},"responses":{"200":{"description":"The smoothed data series","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ApiResponse"}}}},"400":{"description":"Invalid input data"}}}},"/api/series/detect-peaks":{"post":{"summary":"Detect peaks in a 1D data series","description":"Identifies local maxima (peaks) in a 1D data series. More robust and accurate logic.","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"options":{"type":"object","properties":{"smoothWindow":{"type":"integer","description":"Optional window size for Gaussian smoothing to reduce noise before peak detection.","example":3},"minDistance":{"type":"integer","description":"The minimum number of data points between two peaks.","example":1},"threshold":{"type":"number","description":"The minimum value for a data point to be considered a peak.","example":0.5}}}}}}}},"responses":{"200":{"description":"An array of detected peak objects, each with an index and value."},"400":{"description":"Invalid input data"}}}},"/api/series/detect-valleys":{"post":{"summary":"Detect valleys in a 1D data series","description":"Identifies local minima (valleys) in a 1D data series. More robust and accurate logic.","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"options":{"type":"object","properties":{"smoothWindow":{"type":"integer","description":"Optional window size for Gaussian smoothing to reduce noise before valley detection.","example":3},"minDistance":{"type":"integer","description":"The minimum number of data points between two valleys.","example":1},"threshold":{"type":"number","description":"The maximum value for a data point to be considered a valley.","example":-0.5}}}}}}}},"responses":{"200":{"description":"An array of detected valley objects, each with an index and value."},"400":{"description":"Invalid input data"}}}},"/api/series/detect-outliers":{"post":{"summary":"Detect outliers in a 1D data series","description":"Identifies outliers in a 1D data series using statistically sound methods.","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"options":{"type":"object","properties":{"method":{"type":"string","enum":["local_deviation","mean_diff"],"default":"local_deviation"},"windowSize":{"type":"integer","default":7},"threshold":{"type":"number","description":"The sensitivity threshold. For 'local_deviation', this is the number of standard deviations (Z-score).","default":3}}}}}}}},"responses":{"200":{"description":"An array of detected outlier objects.","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ApiResponse"}}}},"400":{"description":"Invalid input data"}}}},"/api/series/detect-vertices":{"post":{"summary":"Detect trend vertices (turning points) in a 1D series","description":"Identifies all significant peaks and valleys in a data series trend using a robust local maxima/minima search.","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"options":{"type":"object","properties":{"smoothingWindow":{"type":"integer","default":5,"description":"Window size for an initial Gaussian smoothing pass to reduce noise."},"threshold":{"type":"number","description":"The absolute value a peak/valley must exceed to be counted.","default":0},"minDistance":{"type":"integer","default":3,"description":"Minimum number of data points between any two vertices."}}}}}}}},"responses":{"200":{"description":"An array of detected vertex objects, labeled as 'peak' or 'valley'.","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ApiResponse"}}}},"400":{"description":"Invalid input data"}}}},"/api/kernels/{name}":{"get":{"summary":"Get a pre-defined convolution kernel","description":"Retrieves a standard 1D or 2D convolution kernel by its name.","tags":["Kernels"],"parameters":[{"in":"path","name":"name","required":true,"schema":{"type":"string","enum":["sobel-x","sobel-y","laplacian","difference1d","average1d"]},"description":"The name of the kernel to retrieve."},{"in":"query","name":"size","schema":{"type":"integer","default":3},"description":"The size of the kernel (for kernels like 'average1d')."}],"responses":{"200":{"description":"The requested kernel as a 1D or 2D array.","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ApiResponse"}}}},"400":{"description":"Unknown kernel name or invalid options."}}}},"/api/docs/export/json":{"get":{"summary":"Export API documentation as JSON","description":"Returns the complete OpenAPI specification in JSON format","tags":["Documentation"],"responses":{"200":{"description":"OpenAPI specification in JSON format","content":{"application/json":{"schema":{"type":"object"}}}}}}},"/api/docs/export/yaml":{"get":{"summary":"Export API documentation as YAML","description":"Returns the complete OpenAPI specification in YAML format","tags":["Documentation"],"responses":{"200":{"description":"OpenAPI specification in YAML format","content":{"text/yaml":{"schema":{"type":"string"}}}}}}},"/api/docs/export/html":{"get":{"summary":"Export API documentation as HTML","description":"Returns a standalone HTML file with the complete API documentation","tags":["Documentation"],"responses":{"200":{"description":"Standalone HTML documentation","content":{"text/html":{"schema":{"type":"string"}}}}}}}},"components":{"schemas":{"DataSeries":{"type":"object","required":["values"],"properties":{"values":{"type":"array","items":{"type":"number"},"description":"Array of numerical values","example":[1,2,3,4,5]},"labels":{"type":"array","items":{"type":"string"},"description":"Optional labels for the values","example":["Jan","Feb","Mar","Apr","May"]}}},"DataMatrix":{"type":"object","required":["data"],"properties":{"data":{"type":"array","items":{"type":"array","items":{"type":"number"}},"description":"2D array of numerical values","example":[[1,2],[3,4],[5,6]]},"columns":{"type":"array","items":{"type":"string"},"description":"Optional column names","example":["x","y"]},"rows":{"type":"array","items":{"type":"string"},"description":"Optional row names","example":["row1","row2","row3"]}}},"Condition":{"type":"object","required":["field","operator","value"],"properties":{"field":{"type":"string","description":"Field name to apply condition on","example":"value"},"operator":{"type":"string","enum":[">","<","=",">=","<=","!="],"description":"Comparison operator","example":">"},"value":{"oneOf":[{"type":"number"},{"type":"string"}],"description":"Value to compare against","example":10}}},"SmoothingOptions":{"type":"object","properties":{"method":{"type":"string","enum":["gaussian","moving_average"],"default":"gaussian","description":"The smoothing method to use."},"windowSize":{"type":"integer","default":5,"description":"The size of the window for the filter. 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Values below this will be set to 0."}}},"ARIMAOptions":{"type":"object","properties":{"p":{"type":"integer","description":"Non-seasonal AutoRegressive (AR) order."},"d":{"type":"integer","description":"Non-seasonal Differencing (I) order."},"q":{"type":"integer","description":"Non-seasonal Moving Average (MA) order."},"P":{"type":"integer","description":"Seasonal AR order."},"D":{"type":"integer","description":"Seasonal Differencing order."},"Q":{"type":"integer","description":"Seasonal MA order."},"s":{"type":"integer","description":"The seasonal period length (e.g., 7 for weekly)."}}},"ApiResponse":{"type":"object","properties":{"success":{"type":"boolean","description":"Whether the request was successful"},"data":{"description":"Response data (varies by endpoint)"},"error":{"type":"string","description":"Error message if success is false"}}}}},"tags":[]},
+                spec: {"openapi":"3.0.0","info":{"title":"My Express API","version":"1.0.0","description":"API documentation for my awesome Express app"},"servers":[{"url":"http://localhost:3000"}],"paths":{"/api/health":{"get":{"summary":"Health check endpoint","description":"Returns the health status of the API","tags":["Health"],"responses":{"200":{"description":"API is healthy","content":{"application/json":{"schema":{"type":"object","properties":{"status":{"type":"string","example":"OK"},"timestamp":{"type":"string","format":"date-time"}}}}}}}}},"/api/unique":{"post":{"summary":"Get unique values from a data series","description":"Returns an array of unique values from the provided data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"}}}}}},"responses":{"200":{"description":"Unique values calculated successfully"},"400":{"description":"Invalid input data"}}}},"/api/mean":{"post":{"summary":"Calculate mean of a data series","description":"Returns the arithmetic mean of the provided data series, optionally filtered by conditions","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Mean calculated successfully"},"400":{"description":"Invalid input data"}}}},"/api/count":{"post":{"summary":"Count data points in a series","description":"Returns the count of data points in the series, optionally filtered by conditions","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Count calculated successfully"},"400":{"description":"Invalid input data"}}}},"/api/variance":{"post":{"summary":"Calculate variance of a data series","description":"Returns the variance of the provided data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Variance calculated successfully"},"400":{"description":"Invalid input data"}}}},"/api/std":{"post":{"summary":"Calculate standard deviation of a data series","description":"Returns the standard deviation of the provided data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Standard deviation calculated successfully"},"400":{"description":"Invalid input data"}}}},"/api/percentile":{"post":{"summary":"Calculate percentile of a data series","description":"Returns the specified percentile of the provided data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"percent":{"type":"number","description":"Percentile to calculate (0-100)","example":95},"ascending":{"type":"boolean","description":"Sort order","default":true},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Percentile calculated successfully"},"400":{"description":"Invalid input data"}}}},"/api/median":{"post":{"summary":"Calculate median of a data series","description":"Returns the median (50th percentile) of the provided data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Median calculated successfully"},"400":{"description":"Invalid input data"}}}},"/api/mode":{"post":{"summary":"Calculate mode of a data series","description":"Returns the mode (most frequent values) of the provided data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Mode calculated successfully"},"400":{"description":"Invalid input data"}}}},"/api/max":{"post":{"summary":"Find maximum value in a data series","description":"Returns the maximum value from the provided data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Maximum value found successfully"},"400":{"description":"Invalid input data"}}}},"/api/min":{"post":{"summary":"Find minimum value in a data series","description":"Returns the minimum value from the provided data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"conditions":{"type":"array","items":{"$ref":"#/components/schemas/Condition"}}}}}}},"responses":{"200":{"description":"Minimum value found successfully"},"400":{"description":"Invalid input data"}}}},"/api/correlation":{"post":{"summary":"Calculate correlation between two data series","description":"Returns the Pearson correlation coefficient between two data series","tags":["Statistics"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series1":{"$ref":"#/components/schemas/DataSeries"},"series2":{"$ref":"#/components/schemas/DataSeries"}}}}}},"responses":{"200":{"description":"Correlation calculated successfully"},"400":{"description":"Invalid input data or series have different lengths"}}}},"/api/series/moving-average":{"post":{"summary":"Calculate moving average of a data series","description":"Returns the moving average of the provided data series with specified window size","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"windowSize":{"type":"integer","description":"Size of the moving window","minimum":1,"example":5}}}}}},"responses":{"200":{"description":"Moving average calculated successfully"},"400":{"description":"Invalid input data or window size"}}}},"/api/series/rolling":{"post":{"summary":"Get rolling windows of a data series","description":"Returns rolling windows of the provided data series with specified window size","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"windowSize":{"type":"integer","description":"Size of the rolling window","minimum":1,"example":3}}}}}},"responses":{"200":{"description":"Rolling windows calculated successfully"},"400":{"description":"Invalid input data or window size"}}}},"/api/ml/kmeans":{"post":{"summary":"Perform K-means clustering","description":"Performs K-means clustering on the provided data matrix","tags":["Machine Learning"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"matrix":{"$ref":"#/components/schemas/DataMatrix"},"nClusters":{"type":"integer","description":"Number of clusters","minimum":1,"example":3},"options":{"type":"object","description":"K-means options"}}}}}},"responses":{"200":{"description":"K-means clustering completed successfully"},"400":{"description":"Invalid input data"}}}},"/api/time/week-number":{"post":{"summary":"Get week number from date","description":"Returns the ISO week number for the provided date string","tags":["Time"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"date":{"type":"string","format":"date","description":"Date string in ISO format","example":"2024-03-15"}}}}}},"responses":{"200":{"description":"Week number calculated successfully"},"400":{"description":"Invalid date format"}}}},"/api/time/same-day-last-year":{"post":{"summary":"Get same day of week from last year","description":"Returns the date string for the same day of the week from the previous year","tags":["Time"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"date":{"type":"string","format":"date","description":"Date string in ISO format","example":"2024-03-15"}}}}}},"responses":{"200":{"description":"Same day last year calculated successfully"},"400":{"description":"Invalid date format"}}}},"/api/retail/purchase-rate":{"post":{"summary":"Calculate purchase rate","description":"Calculates the purchase rate as a percentage of product purchases over total transactions","tags":["Retail"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"productPurchases":{"type":"number","description":"Number of product purchases","example":150},"totalTransactions":{"type":"number","description":"Total number of transactions","example":1000}}}}}},"responses":{"200":{"description":"Purchase rate calculated successfully"},"400":{"description":"Invalid input data or division by zero"}}}},"/api/retail/lift-value":{"post":{"summary":"Calculate lift value","description":"Calculates the lift value for market basket analysis","tags":["Retail"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"jointPurchaseRate":{"type":"number","description":"Joint purchase rate of both products","example":0.05},"productAPurchaseRate":{"type":"number","description":"Purchase rate of product A","example":0.2},"productBPurchaseRate":{"type":"number","description":"Purchase rate of product B","example":0.3}}}}}},"responses":{"200":{"description":"Lift value calculated successfully"},"400":{"description":"Invalid input data or division by zero"}}}},"/api/retail/cost-ratio":{"post":{"summary":"Calculate cost ratio","description":"Calculates the cost ratio (cost divided by sale price)","tags":["Retail"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"cost":{"type":"number","description":"Cost of the product","example":50},"salePrice":{"type":"number","description":"Sale price of the product","example":100}}}}}},"responses":{"200":{"description":"Cost ratio calculated successfully"},"400":{"description":"Invalid input data or division by zero"}}}},"/api/retail/gross-margin":{"post":{"summary":"Calculate gross margin rate","description":"Calculates the gross margin rate as a percentage","tags":["Retail"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"salePrice":{"type":"number","description":"Sale price of the product","example":100},"cost":{"type":"number","description":"Cost of the product","example":60}}}}}},"responses":{"200":{"description":"Gross margin rate calculated successfully"},"400":{"description":"Invalid input data or division by zero"}}}},"/api/retail/average-spend":{"post":{"summary":"Calculate average spend per customer","description":"Calculates the average amount spent per customer","tags":["Retail"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"totalRevenue":{"type":"number","description":"Total revenue","example":50000},"numberOfCustomers":{"type":"number","description":"Number of customers","example":500}}}}}},"responses":{"200":{"description":"Average spend calculated successfully"},"400":{"description":"Invalid input data or division by zero"}}}},"/api/retail/purchase-index":{"post":{"summary":"Calculate purchase index","description":"Calculates the purchase index (items per 1000 customers)","tags":["Retail"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"totalItemsSold":{"type":"number","description":"Total number of items sold","example":2500},"numberOfCustomers":{"type":"number","description":"Number of customers","example":1000}}}}}},"responses":{"200":{"description":"Purchase index calculated successfully"},"400":{"description":"Invalid input data or division by zero"}}}},"/api/predict/forecast":{"post":{"summary":"Generate time series forecast","description":"Generates a forecast for time series data using linear regression","tags":["Prediction"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"forecastPeriods":{"type":"integer","description":"Number of periods to forecast","minimum":1,"example":10}}}}}},"responses":{"200":{"description":"Forecast generated successfully"},"400":{"description":"Invalid input data"}}}},"/api/series/smooth":{"post":{"summary":"Smooth a 1D data series","description":"Applies a smoothing filter (Gaussian or Moving Average) to a 1D data series to reduce noise.","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"options":{"$ref":"#/components/schemas/SmoothingOptions"}}}}}},"responses":{"200":{"description":"The smoothed data series","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ApiResponse"}}}},"400":{"description":"Invalid input data"}}}},"/api/series/detect-peaks":{"post":{"summary":"Detect peaks in a 1D data series","description":"Identifies local maxima (peaks) in a 1D data series. More robust and accurate logic.","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"options":{"type":"object","properties":{"smoothWindow":{"type":"integer","description":"Optional window size for Gaussian smoothing to reduce noise before peak detection.","example":3},"minDistance":{"type":"integer","description":"The minimum number of data points between two peaks.","example":1},"threshold":{"type":"number","description":"The minimum value for a data point to be considered a peak.","example":0.5}}}}}}}},"responses":{"200":{"description":"An array of detected peak objects, each with an index and value."},"400":{"description":"Invalid input data"}}}},"/api/series/detect-valleys":{"post":{"summary":"Detect valleys in a 1D data series","description":"Identifies local minima (valleys) in a 1D data series. More robust and accurate logic.","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"options":{"type":"object","properties":{"smoothWindow":{"type":"integer","description":"Optional window size for Gaussian smoothing to reduce noise before valley detection.","example":3},"minDistance":{"type":"integer","description":"The minimum number of data points between two valleys.","example":1},"threshold":{"type":"number","description":"The maximum value for a data point to be considered a valley.","example":-0.5}}}}}}}},"responses":{"200":{"description":"An array of detected valley objects, each with an index and value."},"400":{"description":"Invalid input data"}}}},"/api/series/detect-outliers":{"post":{"summary":"Detect outliers in a 1D data series","description":"Identifies outliers in a 1D data series using statistically sound methods.","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"options":{"type":"object","properties":{"method":{"type":"string","enum":["local_deviation","mean_diff"],"default":"local_deviation"},"windowSize":{"type":"integer","default":7},"threshold":{"type":"number","description":"The sensitivity threshold. For 'local_deviation', this is the number of standard deviations (Z-score).","default":3}}}}}}}},"responses":{"200":{"description":"An array of detected outlier objects.","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ApiResponse"}}}},"400":{"description":"Invalid input data"}}}},"/api/series/detect-vertices":{"post":{"summary":"Detect trend vertices (turning points) in a 1D series","description":"Identifies all significant peaks and valleys in a data series trend using a robust local maxima/minima search.","tags":["Series Operations"],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"series":{"$ref":"#/components/schemas/DataSeries"},"options":{"type":"object","properties":{"smoothingWindow":{"type":"integer","default":5,"description":"Window size for an initial Gaussian smoothing pass to reduce noise."},"threshold":{"type":"number","description":"The absolute value a peak/valley must exceed to be counted.","default":0},"minDistance":{"type":"integer","default":3,"description":"Minimum number of data points between any two vertices."}}}}}}}},"responses":{"200":{"description":"An array of detected vertex objects, labeled as 'peak' or 'valley'.","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ApiResponse"}}}},"400":{"description":"Invalid input data"}}}},"/api/kernels/{name}":{"get":{"summary":"Get a pre-defined convolution kernel","description":"Retrieves a standard 1D or 2D convolution kernel by its name.","tags":["Kernels"],"parameters":[{"in":"path","name":"name","required":true,"schema":{"type":"string","enum":["sobel-x","sobel-y","laplacian","difference1d","average1d"]},"description":"The name of the kernel to retrieve."},{"in":"query","name":"size","schema":{"type":"integer","default":3},"description":"The size of the kernel (for kernels like 'average1d')."}],"responses":{"200":{"description":"The requested kernel as a 1D or 2D array.","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ApiResponse"}}}},"400":{"description":"Unknown kernel name or invalid options."}}}},"/api/docs/export/json":{"get":{"summary":"Export API documentation as JSON","description":"Returns the complete OpenAPI specification in JSON format","tags":["Documentation"],"responses":{"200":{"description":"OpenAPI specification in JSON format","content":{"application/json":{"schema":{"type":"object"}}}}}}},"/api/docs/export/yaml":{"get":{"summary":"Export API documentation as YAML","description":"Returns the complete OpenAPI specification in YAML format","tags":["Documentation"],"responses":{"200":{"description":"OpenAPI specification in YAML format","content":{"text/yaml":{"schema":{"type":"string"}}}}}}},"/api/docs/export/html":{"get":{"summary":"Export API documentation as HTML","description":"Returns a standalone HTML file with the complete API documentation","tags":["Documentation"],"responses":{"200":{"description":"Standalone HTML documentation","content":{"text/html":{"schema":{"type":"string"}}}}}}}},"components":{"schemas":{"DataSeries":{"type":"object","required":["values"],"properties":{"values":{"type":"array","items":{"type":"number"},"description":"Array of numerical values","example":[1,2,3,4,5]},"labels":{"type":"array","items":{"type":"string"},"description":"Optional labels for the values","example":["Jan","Feb","Mar","Apr","May"]}}},"DataMatrix":{"type":"object","required":["data"],"properties":{"data":{"type":"array","items":{"type":"array","items":{"type":"number"}},"description":"2D array of numerical values","example":[[1,2],[3,4],[5,6]]},"columns":{"type":"array","items":{"type":"string"},"description":"Optional column names","example":["x","y"]},"rows":{"type":"array","items":{"type":"string"},"description":"Optional row names","example":["row1","row2","row3"]}}},"Condition":{"type":"object","required":["field","operator","value"],"properties":{"field":{"type":"string","description":"Field name to apply condition on","example":"value"},"operator":{"type":"string","enum":[">","<","=",">=","<=","!="],"description":"Comparison operator","example":">"},"value":{"oneOf":[{"type":"number"},{"type":"string"}],"description":"Value to compare against","example":10}}},"SmoothingOptions":{"type":"object","properties":{"method":{"type":"string","enum":["gaussian","moving_average"],"default":"gaussian","description":"The smoothing method to use."},"windowSize":{"type":"integer","default":5,"description":"The size of the window for the filter. Must be an odd number for Gaussian."},"sigma":{"type":"number","default":1,"description":"The standard deviation for the Gaussian filter."}}},"EdgeDetectionOptions":{"type":"object","properties":{"method":{"type":"string","enum":["sobel","laplacian"],"default":"sobel","description":"The edge detection algorithm to use."},"threshold":{"type":"number","default":0.1,"description":"The sensitivity threshold for detecting an edge. Values below this will be set to 0."}}},"ApiResponse":{"type":"object","properties":{"success":{"type":"boolean","description":"Whether the request was successful"},"data":{"description":"Response data (varies by endpoint)"},"error":{"type":"string","description":"Error message if success is false"}}}}},"tags":[]},
                 dom_id: '#swagger-ui',
                 deepLinking: true,
                 presets: [
diff --git a/server.ts b/server.ts
index 4fefdd5..4991752 100644
--- a/server.ts
+++ b/server.ts
@@ -8,20 +8,19 @@ import swaggerJsdoc from 'swagger-jsdoc';
 import swaggerUi from 'swagger-ui-express';
 import * as math from 'mathjs';
 import * as _ from 'lodash';
-import cors from 'cors'; // <-- 1. IMPORT THE CORS PACKAGE
 
-// Assuming these files exist in the same directory
+// These imports assume the files exist in the same directory
 // import { KMeans, KMeansOptions } from './kmeans';
 // import { getWeekNumber, getSameWeekDayLastYear } from './time-helper';
 // import { calculateLinearRegression, generateForecast, calculatePredictionIntervals, ForecastResult } from './prediction';
 import { SignalProcessor, SmoothingOptions, EdgeDetectionOptions } from './signal_processing_convolution';
-import { TimeSeriesAnalyzer, ARIMAOptions } from './timeseries';
-import { AnalysisPipelines } from './analysis_pipelines';
-import { convolve1D, convolve2D, ConvolutionKernels } from './convolution';
+import { convolve1D, ConvolutionKernels } from './convolution'; // Direct import for new functions
 
-// Dummy interfaces/classes if the files are not present, to prevent compile errors
 interface KMeansOptions {}
-class KMeans { constructor(p: any, n: any, o: any) {}; run = () => ({ clusters: [] }) }
+class KMeans { 
+    constructor(p: any, n: any, o: any) {}
+    run = () => ({ clusters: [] }) 
+}
 const getWeekNumber = (d: string) => 1;
 const getSameWeekDayLastYear = (d: string) => new Date().toISOString();
 interface ForecastResult {}
@@ -29,27 +28,25 @@ const calculateLinearRegression = (v: any) => ({slope: 1, intercept: 0});
 const generateForecast = (m: any, l: any, p: any) => [];
 const calculatePredictionIntervals = (v: any, m: any, f: any) => [];
 
-
 const app = express();
 app.use(express.json());
-app.use(cors()); // <-- 2. ENABLE CORS FOR ALL ROUTES
 const PORT = process.env.PORT || 3000;
 
 const swaggerOptions = {
-  swaggerDefinition: {
-    openapi: '3.0.0',
-    info: {
-      title: 'My Express API',
-      version: '1.0.0',
-      description: 'API documentation for my awesome Express app',
+    swaggerDefinition: {
+        openapi: '3.0.0',
+        info: {
+            title: 'My Express API',
+            version: '1.0.0',
+            description: 'API documentation for my awesome Express app',
+        },
+        servers: [
+            {
+                url: `http://localhost:${PORT}`,
+            },
+        ],
     },
-    servers: [
-      {
-        url: `http://localhost:${PORT}`,
-      },
-    ],
-  },
-  apis: ["./server.ts"], // Pointing to the correct, renamed file
+    apis: ["./server.ts"], // Pointing to this file for Swagger docs
 };
 
 const swaggerSpec = swaggerJsdoc(swaggerOptions);
@@ -70,6 +67,7 @@ interface DataMatrix {
     columns?: string[];
     rows?: string[];
 }
+
 interface Condition {
     field: string;
     operator: '>' | '<' | '=' | '>=' | '<=' | '!=';
@@ -87,8 +85,9 @@ interface ApiResponse<T> {
 // ========================================
 
 const handleError = (error: unknown): string => {
-  return error instanceof Error ? error.message : 'Unknown error';
+    return error instanceof Error ? error.message : 'Unknown error';
 };
+
 const validateSeries = (series: DataSeries): void => {
     if (!series || !Array.isArray(series.values) || series.values.length === 0) {
         throw new Error('Series must contain at least one value');
@@ -855,159 +854,6 @@ app.post('/api/series/rolling', (req, res) => {
     }
 });
 
-/**
- * @swagger
- * /api/series/auto-arima-find:
- *   post:
- *     summary: (EXPERIMENTAL) Automatically find best SARIMA parameters
- *     description: Performs a grid search to find the best SARIMA parameters based on AIC. NOTE - This is a simplified estimation and may not find the true optimal model. For best results, use the identification tools and the 'manual-forecast' endpoint.
- *     tags: [Series Operations]
- *     requestBody:
- *       required: true
- *       content:
- *         application/json:
- *           schema:
- *             type: object
- *             properties:
- *               series:
- *                 $ref: '#/components/schemas/DataSeries'
- *               seasonalPeriod:
- *                 type: integer
- *                 description: The seasonal period of the data (e.g., 7 for weekly).
- *                 example: 7
- *     responses:
- *       '200':
- *         description: The best model found and the search log.
- *       '400':
- *         description: Invalid input data.
- */
-app.post('/api/series/auto-arima-find', (req, res) => {
-    try {
-        const { series, seasonalPeriod } = req.body;
-        validateSeries(series);
-        const result = AnalysisPipelines.findBestArimaParameters(series.values, seasonalPeriod);
-        res.status(200).json({ success: true, data: result });
-    } catch (error) {
-        const errorMessage = handleError(error);
-        res.status(400).json({ success: false, error: errorMessage });
-    }
-});
-
-/**
- * @swagger
- * /api/series/manual-forecast:
- *   post:
- *     summary: Generate a forecast with manually specified SARIMA parameters
- *     description: This is the primary forecasting tool. It allows an expert user (who has analyzed ACF/PACF plots) to apply a specific SARIMA model to a time series and generate a forecast.
- *     tags: [Series Operations]
- *     requestBody:
- *       required: true
- *       content:
- *         application/json:
- *           schema:
- *             type: object
- *             properties:
- *               series:
- *                 $ref: '#/components/schemas/DataSeries'
- *               options:
- *                 $ref: '#/components/schemas/ARIMAOptions'
- *               forecastSteps:
- *                 type: integer
- *                 description: The number of future time steps to predict.
- *                 example: 7
- *     responses:
- *       '200':
- *         description: The forecast results.
- *       '400':
- *         description: Invalid input data
- */
-app.post('/api/series/manual-forecast', (req, res) => {
-    try {
-        const { series, options, forecastSteps } = req.body;
-        validateSeries(series);
-        const result = TimeSeriesAnalyzer.arimaForecast(series.values, options, forecastSteps);
-        res.status(200).json({ success: true, data: result });
-    } catch (error) {
-        const errorMessage = handleError(error);
-        res.status(400).json({ success: false, error: errorMessage });
-    }
-});
-
-/**
- * @swagger
- * /api/series/identify-correlations:
- *   post:
- *     summary: Calculate ACF and PACF for a time series
- *     description: Returns the Autocorrelation and Partial Autocorrelation function values, which are essential for identifying SARIMA model parameters.
- *     tags: [Series Operations]
- *     requestBody:
- *       required: true
- *       content:
- *         application/json:
- *           schema:
- *             type: object
- *             properties:
- *               series:
- *                 $ref: '#/components/schemas/DataSeries'
- *               maxLag:
- *                 type: integer
- *                 description: The maximum number of lags to calculate.
- *                 example: 40
- *   responses:
- *     '200':
- *       description: The calculated ACF and PACF values.
- *     '400':
- *       description: Invalid input data.
- */
-app.post('/api/series/identify-correlations', (req, res) => {
-    try {
-        const { series, maxLag } = req.body;
-        validateSeries(series);
-        const acf = TimeSeriesAnalyzer.calculateACF(series.values, maxLag);
-        const pacf = TimeSeriesAnalyzer.calculatePACF(series.values, maxLag);
-        res.status(200).json({ success: true, data: { acf, pacf } });
-    } catch (error) {
-        res.status(400).json({ success: false, error: handleError(error) });
-    }
-});
-
-/**
- * @swagger
- * /api/series/decompose-stl:
- *   post:
- *     summary: Decompose a time series into components
- *     description: Applies Seasonal-Trend-Loess (STL) decomposition to separate the series into trend, seasonal, and residual components.
- *     tags: [Series Operations]
- *     requestBody:
- *       required: true
- *       content:
- *         application/json:
- *           schema:
- *             type: object
- *             properties:
- *               series:
- *                 $ref: '#/components/schemas/DataSeries'
- *               period:
- *                 type: integer
- *                 description: The seasonal period of the data (e.g., 7 for weekly).
- *                 example: 7
- *   responses:
- *     '200':
- *       description: The decomposed components of the time series.
- *     '400':
- *       description: Invalid input data.
- */
-app.post('/api/series/decompose-stl', (req, res) => {
-    try {
-        const { series, period } = req.body;
-        validateSeries(series);
-        const result = TimeSeriesAnalyzer.stlDecomposition(series.values, period);
-        res.status(200).json({ success: true, data: result });
-    } catch (error) {
-        res.status(400).json({ success: false, error: handleError(error) });
-    }
-});
-
 /**
  * @swagger
  * /api/ml/kmeans:
@@ -1802,30 +1648,6 @@ app.get('/api/kernels/:name', (req, res) => {
  *           type: number
  *           default: 0.1
  *           description: The sensitivity threshold for detecting an edge. Values below this will be set to 0.
- *     ARIMAOptions:
- *       type: object
- *       properties:
- *         p:
- *           type: integer
- *           description: Non-seasonal AutoRegressive (AR) order.
- *         d:
- *           type: integer
- *           description: Non-seasonal Differencing (I) order.
- *         q:
- *           type: integer
- *           description: Non-seasonal Moving Average (MA) order.
- *         P:
- *           type: integer
- *           description: Seasonal AR order.
- *         D:
- *           type: integer
- *           description: Seasonal Differencing order.
- *         Q:
- *           type: integer
- *           description: Seasonal MA order.
- *         s:
- *           type: integer
- *           description: The seasonal period length (e.g., 7 for weekly).
  *     ApiResponse:
  *       type: object
  *       properties:
@@ -1958,7 +1780,7 @@ app.get('/api/docs/export/html', (req, res) => {
 
 app.use((err: Error, req: express.Request, res: express.Response, next: express.NextFunction) => {
     console.error(err.stack);
-    res.status(500).json({ success: false, error: 'Internal server error' });
+    res.status(500).json({ success: false, error: 'Internal server error' } as ApiResponse<any>);
 });
 
 app.use('*', (req, res) => {
@@ -1970,8 +1792,9 @@ app.use('*', (req, res) => {
 // ========================================
 
 app.listen(PORT, () => {
-  console.log(`Analytics API server running on port ${PORT}`);
-  console.log(`API Documentation: http://localhost:${PORT}/api-docs`);
+    console.log(`Analytics API server running on port ${PORT}`);
+    console.log(`Health check: http://localhost:${PORT}/api/health`);
+    console.log(`API Documentation: http://localhost:${PORT}/api-docs`);
 });
 
 export default app;
\ No newline at end of file
diff --git a/timeseries.ts b/timeseries.ts
deleted file mode 100644
index 077c81f..0000000
--- a/timeseries.ts
+++ /dev/null
@@ -1,346 +0,0 @@
-// timeseries.ts - A library for time series analysis, focusing on ARIMA.
-
-// ========================================
-// TYPE DEFINITIONS
-// ========================================
-
-/**
- * Defines the parameters for an ARIMA model.
- * (p, d, q) are the non-seasonal components.
- * (P, D, Q, s) are the optional seasonal components for SARIMA.
- */
-export interface ARIMAOptions {
-  p: number; // AutoRegressive (AR) order
-  d: number; // Differencing (I) order
-  q: number; // Moving Average (MA) order
-  P?: number; // Seasonal AR order
-  D?: number; // Seasonal Differencing order
-  Q?: number; // Seasonal MA order
-  s?: number; // Seasonal period length
-}
-
-/**
- * The result object from an ARIMA forecast.
- */
-export interface ARIMAForecastResult {
-  forecast: number[]; // The predicted future values
-  residuals: number[]; // The errors of the model fit on the original data
-  model: ARIMAOptions; // The model parameters used
-}
-
-/**
- * The result object from an STL decomposition.
- */
-export interface STLDecomposition {
-  seasonal: number[]; // The seasonal component of the series
-  trend: number[];    // The trend component of the series
-  residual: number[]; // The remainder/residual component
-  original: number[]; // The original series, for comparison
-}
-
-
-/**
- * A class for performing time series analysis, including identification and forecasting.
- */
-export class TimeSeriesAnalyzer {
-
-  // ========================================
-  // 1. IDENTIFICATION METHODS
-  // ========================================
-
-  /**
-   * Calculates the difference of a time series.
-   * This is the 'I' (Integrated) part of ARIMA, used to make a series stationary.
-   * @param series The input data series.
-   * @param lag The lag to difference by (usually 1).
-   * @returns A new, differenced time series.
-   */
-  static difference(series: number[], lag: number = 1): number[] {
-    if (lag < 1 || !Number.isInteger(lag)) {
-      throw new Error('Lag must be a positive integer.');
-    }
-    if (series.length <= lag) {
-      return [];
-    }
-
-    const differenced: number[] = [];
-    for (let i = lag; i < series.length; i++) {
-      differenced.push(series[i] - series[i - lag]);
-    }
-    return differenced;
-  }
-  
-  /**
-   * Helper function to calculate the autocovariance of a series at a given lag.
-   */
-  private static autocovariance(series: number[], lag: number): number {
-    const n = series.length;
-    if (lag >= n) return 0;
-    const mean = series.reduce((a, b) => a + b) / n;
-    let sum = 0;
-    for (let i = lag; i < n; i++) {
-      sum += (series[i] - mean) * (series[i - lag] - mean);
-    }
-    return sum / n;
-  }
-
-  /**
-   * Calculates the Autocorrelation Function (ACF) for a time series.
-   * ACF helps in determining the 'q' parameter for an ARIMA model.
-   * @param series The input data series.
-   * @param maxLag The maximum number of lags to calculate.
-   * @returns An array of correlation values from lag 1 to maxLag.
-   */
-  static calculateACF(series: number[], maxLag: number): number[] {
-    if (series.length < 2) return [];
-    
-    const variance = this.autocovariance(series, 0);
-    if (variance === 0) {
-      return new Array(maxLag).fill(1);
-    }
-
-    const acf: number[] = [];
-    for (let lag = 1; lag <= maxLag; lag++) {
-      acf.push(this.autocovariance(series, lag) / variance);
-    }
-    return acf;
-  }
-
-  /**
-   * Calculates the Partial Autocorrelation Function (PACF) for a time series.
-   * This now uses the Durbin-Levinson algorithm for an accurate calculation.
-   * PACF helps in determining the 'p' parameter for an ARIMA model.
-   * @param series The input data series.
-   * @param maxLag The maximum number of lags to calculate.
-   * @returns An array of partial correlation values from lag 1 to maxLag.
-   */
-  static calculatePACF(series: number[], maxLag: number): number[] {
-    const acf = this.calculateACF(series, maxLag);
-    const pacf: number[] = [];
-    
-    if (acf.length === 0) return [];
-
-    pacf.push(acf[0]); // PACF at lag 1 is the same as ACF at lag 1
-
-    for (let k = 2; k <= maxLag; k++) {
-      let numerator = acf[k - 1];
-      let denominator = 1;
-
-      const phi = new Array(k + 1).fill(0).map(() => new Array(k + 1).fill(0));
-      
-      for(let i=1; i<=k; i++) {
-        phi[i][i] = acf[i-1];
-      }
-
-      for (let j = 1; j < k; j++) {
-        const factor = pacf[j - 1];
-        numerator -= factor * acf[k - j - 1];
-        denominator -= factor * acf[j - 1];
-      }
-      
-      if (Math.abs(denominator) < 1e-9) { // Avoid division by zero
-        pacf.push(0);
-        continue;
-      }
-      
-      const pacf_k = numerator / denominator;
-      pacf.push(pacf_k);
-    }
-
-    return pacf;
-  }
-
-  /**
-   * Decomposes a time series using the robust Classical Additive method.
-   * This version correctly isolates trend, seasonal, and residual components.
-   * @param series The input data series.
-   * @param period The seasonal period (e.g., 7 for daily data with a weekly cycle).
-   * @returns An object containing the seasonal, trend, and residual series.
-   */
-  static stlDecomposition(series: number[], period: number): STLDecomposition {
-    if (series.length < 2 * period) {
-      throw new Error("Series must be at least twice the length of the seasonal period.");
-    }
-    
-    // Helper for a centered moving average
-    const movingAverage = (data: number[], window: number) => {
-        const result = [];
-        const halfWindow = Math.floor(window / 2);
-        for (let i = 0; i < data.length; i++) {
-            const start = Math.max(0, i - halfWindow);
-            const end = Math.min(data.length, i + halfWindow + 1);
-            let sum = 0;
-            for (let j = start; j < end; j++) {
-                sum += data[j];
-            }
-            result.push(sum / (end - start));
-        }
-        return result;
-    };
-
-    // Step 1: Calculate the trend using a centered moving average.
-    // If period is even, we use a 2x-MA to center it correctly.
-    let trend: number[];
-    if (period % 2 === 0) {
-        const intermediate = movingAverage(series, period);
-        trend = movingAverage(intermediate, 2);
-    } else {
-        trend = movingAverage(series, period);
-    }
-    
-    // Step 2: Detrend the series
-    const detrended = series.map((val, i) => val - trend[i]);
-
-    // Step 3: Calculate the seasonal component by averaging the detrended values for each period
-    const seasonalAverages = new Array(period).fill(0);
-    const seasonalCounts = new Array(period).fill(0);
-    for (let i = 0; i < series.length; i++) {
-        if (!isNaN(detrended[i])) {
-            const seasonIndex = i % period;
-            seasonalAverages[seasonIndex] += detrended[i];
-            seasonalCounts[seasonIndex]++;
-        }
-    }
-    
-    for (let i = 0; i < period; i++) {
-        seasonalAverages[i] /= seasonalCounts[i];
-    }
-    
-    // Center the seasonal component to have a mean of zero
-    const seasonalMean = seasonalAverages.reduce((a, b) => a + b, 0) / period;
-    const centeredSeasonalAverages = seasonalAverages.map(avg => avg - seasonalMean);
-
-    const seasonal = new Array(series.length).fill(0);
-    for (let i = 0; i < series.length; i++) {
-        seasonal[i] = centeredSeasonalAverages[i % period];
-    }
-
-    // Step 4: Calculate the residual component
-    const residual = detrended.map((val, i) => val - seasonal[i]);
-
-    return {
-      original: series,
-      seasonal,
-      trend,
-      residual,
-    };
-  }
-
-
-  // ========================================
-  // 2. FORECASTING METHODS
-  // ========================================
-
-  /**
-   * [UPGRADED] Generates a forecast using a simplified SARIMA model.
-   * This implementation now handles both non-seasonal (p,d,q) and seasonal (P,D,Q,s) components.
-   * @param series The input time series data.
-   * @param options The SARIMA parameters.
-   * @param forecastSteps The number of future steps to predict.
-   * @returns An object containing the forecast and model residuals.
-   */
-  static arimaForecast(series: number[], options: ARIMAOptions, forecastSteps: number): ARIMAForecastResult {
-    const { p, d, q, P = 0, D = 0, Q = 0, s = 0 } = options;
-
-    if (series.length < p + d + (P + D) * s + q + Q * s) {
-      throw new Error("Data series is too short for the specified SARIMA order.");
-    }
-    
-    const originalSeries = [...series];
-    let differencedSeries = [...series];
-    const diffLog: { lag: number, values: number[] }[] = [];
-
-    // Step 1: Apply seasonal differencing 'D' times
-    for (let i = 0; i < D; i++) {
-        diffLog.push({ lag: s, values: differencedSeries.slice(-s) });
-        differencedSeries = this.difference(differencedSeries, s);
-    }
-    
-    // Step 2: Apply non-seasonal differencing 'd' times
-    for (let i = 0; i < d; i++) {
-        diffLog.push({ lag: 1, values: differencedSeries.slice(-1) });
-        differencedSeries = this.difference(differencedSeries, 1);
-    }
-
-    const n = differencedSeries.length;
-    // Simplified coefficients
-    const arCoeffs = p > 0 ? new Array(p).fill(1 / p) : [];
-    const maCoeffs = q > 0 ? new Array(q).fill(1 / q) : [];
-    const sarCoeffs = P > 0 ? new Array(P).fill(1 / P) : [];
-    const smaCoeffs = Q > 0 ? new Array(Q).fill(1 / Q) : [];
-    
-    const residuals: number[] = new Array(n).fill(0);
-    const fitted: number[] = new Array(n).fill(0);
-
-    // Step 3: Fit the model
-    const startIdx = Math.max(p, q, P * s, Q * s);
-    for (let t = startIdx; t < n; t++) {
-        // Non-seasonal AR
-        let arVal = 0;
-        for (let i = 0; i < p; i++) arVal += arCoeffs[i] * differencedSeries[t - 1 - i];
-
-        // Non-seasonal MA
-        let maVal = 0;
-        for (let i = 0; i < q; i++) maVal += maCoeffs[i] * residuals[t - 1 - i];
-        
-        // Seasonal AR
-        let sarVal = 0;
-        for (let i = 0; i < P; i++) sarVal += sarCoeffs[i] * differencedSeries[t - s * (i + 1)];
-
-        // Seasonal MA
-        let smaVal = 0;
-        for (let i = 0; i < Q; i++) smaVal += smaCoeffs[i] * residuals[t - s * (i + 1)];
-        
-        fitted[t] = arVal + maVal + sarVal + smaVal;
-        residuals[t] = differencedSeries[t] - fitted[t];
-    }
-
-    // Step 4: Generate the forecast
-    const forecastDifferenced: number[] = [];
-    const extendedSeries = [...differencedSeries];
-    const extendedResiduals = [...residuals];
-
-    for (let f = 0; f < forecastSteps; f++) {
-        const t = n + f;
-        let nextForecast = 0;
-        
-        // AR
-        for (let i = 0; i < p; i++) nextForecast += arCoeffs[i] * extendedSeries[t - 1 - i];
-        // MA (future residuals are 0)
-        for (let i = 0; i < q; i++) nextForecast += maCoeffs[i] * extendedResiduals[t - 1 - i];
-        // SAR
-        for (let i = 0; i < P; i++) nextForecast += sarCoeffs[i] * extendedSeries[t - s * (i + 1)];
-        // SMA
-        for (let i = 0; i < Q; i++) nextForecast += smaCoeffs[i] * extendedResiduals[t - s * (i + 1)];
-
-        forecastDifferenced.push(nextForecast);
-        extendedSeries.push(nextForecast);
-        extendedResiduals.push(0);
-    }
-
-    // Step 5: Invert the differencing
-    let forecast = [...forecastDifferenced];
-    for (let i = diffLog.length - 1; i >= 0; i--) {
-        const { lag, values } = diffLog[i];
-        const inverted = [];
-        const fullHistory = [...originalSeries, ...forecast]; // Need a temporary full history for inversion
-        
-        // A simpler inversion method for forecasting
-        let history = [...series];
-        for (const forecastVal of forecast) {
-            const lastSeasonalVal = history[history.length - lag];
-            const invertedVal = forecastVal + lastSeasonalVal;
-            inverted.push(invertedVal);
-            history.push(invertedVal);
-        }
-        forecast = inverted;
-    }
-
-    return {
-      forecast,
-      residuals,
-      model: options,
-    };
-  }
-}
-