diff --git a/api-documentation.html b/api-documentation.html
index 475d326..6da21b0 100644
--- a/api-documentation.html
+++ b/api-documentation.html
@@ -28,7 +28,7 @@
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         window.onload = function() {
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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 b10045c..4991752 100644
--- a/server.ts
+++ b/server.ts
@@ -1244,11 +1244,11 @@ app.post('/api/predict/forecast', (req, res) => {
 
 /**
  * @swagger
- * /api/signal/smooth:
+ * /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: [Signal Processing]
+ *     tags: [Series Operations]
  *     requestBody:
  *       required: true
  *       content:
@@ -1270,7 +1270,7 @@ app.post('/api/predict/forecast', (req, res) => {
  *       '400':
  *         description: Invalid input data
  */
-app.post('/api/signal/smooth', (req, res) => {
+app.post('/api/series/smooth', (req, res) => {
     try {
         const { series, options } = req.body;
         validateSeries(series);
@@ -1284,11 +1284,11 @@ app.post('/api/signal/smooth', (req, res) => {
 
 /**
  * @swagger
- * /api/signal/detect-peaks:
+ * /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: [Signal Processing]
+ *     tags: [Series Operations]
  *     requestBody:
  *       required: true
  *       content:
@@ -1319,7 +1319,7 @@ app.post('/api/signal/smooth', (req, res) => {
  *       '400':
  *         description: Invalid input data
  */
-app.post('/api/signal/detect-peaks', (req, res) => {
+app.post('/api/series/detect-peaks', (req, res) => {
     try {
         const { series, options } = req.body;
         validateSeries(series);
@@ -1333,11 +1333,11 @@ app.post('/api/signal/detect-peaks', (req, res) => {
 
 /**
  * @swagger
- * /api/signal/detect-valleys:
+ * /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: [Signal Processing]
+ *     tags: [Series Operations]
  *     requestBody:
  *       required: true
  *       content:
@@ -1368,7 +1368,7 @@ app.post('/api/signal/detect-peaks', (req, res) => {
  *       '400':
  *         description: Invalid input data
  */
-app.post('/api/signal/detect-valleys', (req, res) => {
+app.post('/api/series/detect-valleys', (req, res) => {
     try {
         const { series, options } = req.body;
         validateSeries(series);
@@ -1382,11 +1382,11 @@ app.post('/api/signal/detect-valleys', (req, res) => {
 
 /**
  * @swagger
- * /api/signal/detect-outliers:
+ * /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: [Signal Processing]
+ *     tags: [Series Operations]
  *     requestBody:
  *       required: true
  *       content:
@@ -1420,7 +1420,7 @@ app.post('/api/signal/detect-valleys', (req, res) => {
  *       '400':
  *         description: Invalid input data
  */
-app.post('/api/signal/detect-outliers', (req, res) => {
+app.post('/api/series/detect-outliers', (req, res) => {
     try {
         const { series, options } = req.body;
         validateSeries(series);
@@ -1434,11 +1434,11 @@ app.post('/api/signal/detect-outliers', (req, res) => {
 
 /**
  * @swagger
- * /api/signal/detect-vertices:
+ * /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: [Signal Processing]
+ *     tags: [Series Operations]
  *     requestBody:
  *       required: true
  *       content:
@@ -1473,7 +1473,7 @@ app.post('/api/signal/detect-outliers', (req, res) => {
  *       '400':
  *         description: Invalid input data
  */
-app.post('/api/signal/detect-vertices', (req, res) => {
+app.post('/api/series/detect-vertices', (req, res) => {
     try {
         const { series, options } = req.body;
         validateSeries(series);