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merge into: raymond:timeseries
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raymond:reconstruct
raymond:timeseries
raymond:convolution
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5 commits
timeseries ... main

Author SHA1 Message Date
RaymondHung-datacom
ca8bded949 reconstruct 2025-09-25 16:28:20 +09:00
raymond
20002030ad Merge pull request 'timeseries branch commit' (#2) from timeseries into main 
Reviewed-on: #2
 2025-09-24 02:36:06 +00:00
raymond
f558be337f Merge pull request 'convolution merge' (#1) from convolution into main 
Reviewed-on: #1
 2025-09-11 00:44:38 +00:00
raymond
0aa6248a9b commit miss, recover server.ts 2025-09-10 06:20:48 +00:00
raymond
1b49ae20fe update server.ts to fit server_convolution.ts 2025-09-10 06:17:31 +00:00
17 changed files with 1268 additions and 1110 deletions
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46
api-documentation.html
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35
package.json Normal file
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@ -0,0 +1,35 @@
{
"name": "analytics-api",
"version": "1.0.0",
"main": "index.js",
"scripts": {
"test": "echo \"Error: no test specified\" && exit 1"
},
"keywords": [],
"author": "",
"license": "ISC",
"description": "",
"dependencies": {
"cors": "^2.8.5",
"date-fns": "^4.1.0",
"express": "^4.21.2",
"lodash": "^4.17.21",
"mathjs": "^14.6.0",
"swagger-ui-express": "^5.0.1"
},
"devDependencies": {
"@types/cors": "^2.8.19",
"@types/express": "^4.17.23",
"@types/jest": "^30.0.0",
"@types/lodash": "^4.17.20",
"@types/node": "^24.3.0",
"@types/swagger-jsdoc": "^6.0.4",
"@types/swagger-ui-express": "^4.1.8",
"concurrently": "^9.2.1",
"jest": "^30.1.3",
"swagger-jsdoc": "^6.2.8",
"ts-jest": "^29.4.4",
"ts-node": "^10.9.2",
"typescript": "^5.9.2"
}
}

400
server.ts
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@ -6,30 +6,25 @@
import express from 'express'; import express from 'express';
import swaggerJsdoc from 'swagger-jsdoc'; import swaggerJsdoc from 'swagger-jsdoc';
import swaggerUi from 'swagger-ui-express'; import swaggerUi from 'swagger-ui-express';
import * as math from 'mathjs'; import cors from 'cors';
import * as _ from 'lodash';
import cors from 'cors'; // <-- 1. IMPORT THE CORS PACKAGE
// Assuming these files exist in the same directory // Assuming these files exist in the same directory
// import { KMeans, KMeansOptions } from './kmeans'; // import { KMeans, KMeansOptions } from './kmeans';
// import { getWeekNumber, getSameWeekDayLastYear } from './time-helper'; // import { getWeekNumber, getSameWeekDayLastYear } from './time-helper';
// import { calculateLinearRegression, generateForecast, calculatePredictionIntervals, ForecastResult } from './prediction'; // import { calculateLinearRegression, generateForecast, calculatePredictionIntervals, ForecastResult } from './prediction';
import { SignalProcessor, SmoothingOptions, EdgeDetectionOptions } from './signal_processing_convolution'; import { SignalProcessor, SmoothingOptions, EdgeDetectionOptions } from './services/signal_processing_convolution';
import { TimeSeriesAnalyzer, ARIMAOptions } from './timeseries'; import { TimeSeriesAnalyzer, ARIMAOptions } from './services/timeseries';
import { AnalysisPipelines } from './analysis_pipelines'; import { AnalysisPipelines } from './services/analysis_pipelines';
import { convolve1D, convolve2D, ConvolutionKernels } from './convolution'; import { convolve1D, convolve2D, ConvolutionKernels } from './services/convolution';
import { DataSeries, DataMatrix, Condition, ApiResponse } from './types/index';
// Dummy interfaces/classes if the files are not present, to prevent compile errors import { handleError, validateSeries, validateMatrix } from './services/analytics_engine';
interface KMeansOptions {} import { ForecastResult } from './services/prediction';
class KMeans { constructor(p: any, n: any, o: any) {}; run = () => ({ clusters: [] }) } import { analytics } from './services/analytics_engine';
const getWeekNumber = (d: string) => 1; import { purchaseRate, liftValue, costRatio, grossMarginRate, averageSpendPerCustomer, purchaseIndex } from './services/retail_metrics';
const getSameWeekDayLastYear = (d: string) => new Date().toISOString(); import { RollingWindow } from './services/rolling_window';
interface ForecastResult {} import { pivotTable, PivotOptions } from './services/pivot_table';
const calculateLinearRegression = (v: any) => ({slope: 1, intercept: 0});
const generateForecast = (m: any, l: any, p: any) => [];
const calculatePredictionIntervals = (v: any, m: any, f: any) => [];
// Initialize Express app
const app = express(); const app = express();
app.use(express.json()); app.use(express.json());
app.use(cors()); // <-- 2. ENABLE CORS FOR ALL ROUTES app.use(cors()); // <-- 2. ENABLE CORS FOR ALL ROUTES
@ -56,301 +51,6 @@ const swaggerSpec = swaggerJsdoc(swaggerOptions);
app.use('/api-docs', swaggerUi.serve, swaggerUi.setup(swaggerSpec)); app.use('/api-docs', swaggerUi.serve, swaggerUi.setup(swaggerSpec));
// ========================================
// TYPE DEFINITIONS
// ========================================
interface DataSeries {
values: number[];
labels?: string[];
}
interface DataMatrix {
data: number[][];
columns?: string[];
rows?: string[];
}
interface Condition {
field: string;
operator: '>' | '<' | '=' | '>=' | '<=' | '!=';
value: number | string;
}
interface ApiResponse<T> {
success: boolean;
data?: T;
error?: string;
}
// ========================================
// HELPER FUNCTIONS
// ========================================
const handleError = (error: unknown): string => {
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');
}
};
const validateMatrix = (matrix: DataMatrix): void => {
if (!matrix || !Array.isArray(matrix.data) || matrix.data.length === 0) {
throw new Error('Matrix must contain at least one row');
}
};
/**
* A helper class to provide a fluent API for rolling window calculations.
*/
class RollingWindow {
private windows: number[][];
constructor(windows: number[][]) {
this.windows = windows;
}
mean(): number[] {
return this.windows.map(window => Number(math.mean(window)));
}
sum(): number[] {
return this.windows.map(window => _.sum(window));
}
min(): number[] {
return this.windows.map(window => Math.min(...window));
}
max(): number[] {
return this.windows.map(window => Math.max(...window));
}
toArray(): number[][] {
return this.windows;
}
}
// ========================================
// ANALYTICS ENGINE (Simplified)
// ========================================
class AnalyticsEngine {
private applyConditions(series: DataSeries, conditions: Condition[] = []): number[] {
if (conditions.length === 0) return series.values;
return series.values; // TODO: Implement filtering
}
// Basic statistical functions
unique(series: DataSeries): number[] {
validateSeries(series);
return _.uniq(series.values);
}
mean(series: DataSeries, conditions: Condition[] = []): number {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
if (filteredValues.length === 0) throw new Error('No data points match conditions');
return Number(math.mean(filteredValues));
}
count(series: DataSeries, conditions: Condition[] = []): number {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
if (filteredValues.length === 0) throw new Error('No data points match conditions');
return filteredValues.length;
}
variance(series: DataSeries, conditions: Condition[] = []): number {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
if (filteredValues.length === 0) throw new Error('No data points match conditions');
return Number(math.variance(filteredValues));
}
standardDeviation(series: DataSeries, conditions: Condition[] = []): number {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
if (filteredValues.length === 0) throw new Error('No data points match conditions');
return Number(math.std(filteredValues));
}
percentile(series: DataSeries, percent: number, ascending: boolean = true, conditions: Condition[] = []): number {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
if (filteredValues.length === 0) throw new Error('No data points match conditions');
const sorted = ascending ? _.sortBy(filteredValues) : _.sortBy(filteredValues).reverse();
const index = (percent / 100) * (sorted.length - 1);
const lower = Math.floor(index);
const upper = Math.ceil(index);
const weight = index % 1;
return sorted[lower] * (1 - weight) + sorted[upper] * weight;
}
median(series: DataSeries, conditions: Condition[] = []): number {
return this.percentile(series, 50, true, conditions);
}
mode(series: DataSeries, conditions: Condition[] = []): number[] {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
const frequency = _.countBy(filteredValues);
const maxFreq = Math.max(...Object.values(frequency));
return Object.keys(frequency)
.filter(key => frequency[key] === maxFreq)
.map(Number);
}
max(series: DataSeries, conditions: Condition[] = []): number {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
if (filteredValues.length === 0) throw new Error('No data points match conditions');
return Math.max(...filteredValues);
}
min(series: DataSeries, conditions: Condition[] = []): number {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
if (filteredValues.length === 0) throw new Error('No data points match conditions');
return Math.min(...filteredValues);
}
correlation(series1: DataSeries, series2: DataSeries): number {
validateSeries(series1);
validateSeries(series2);
if (series1.values.length !== series2.values.length) {
throw new Error('Series must have same length for correlation');
}
const x = series1.values;
const y = series2.values;
const n = x.length;
const sumX = _.sum(x);
const sumY = _.sum(y);
const sumXY = _.sum(x.map((xi, i) => xi * y[i]));
const sumX2 = _.sum(x.map(xi => xi * xi));
const sumY2 = _.sum(y.map(yi => yi * yi));
const numerator = n * sumXY - sumX * sumY;
const denominator = Math.sqrt((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY));
return numerator / denominator;
}
// Rolling window functions
rolling(series: DataSeries, windowSize: number): RollingWindow {
validateSeries(series);
if (windowSize <= 0) {
throw new Error('Window size must be a positive number.');
}
if (series.values.length < windowSize) {
return new RollingWindow([]);
}
const windows: number[][] = [];
for (let i = 0; i <= series.values.length - windowSize; i++) {
const window = series.values.slice(i, i + windowSize);
windows.push(window);
}
return new RollingWindow(windows);
}
movingAverage(series: DataSeries, windowSize: number): number[] {
return this.rolling(series, windowSize).mean();
}
// K-means wrapper (uses imported KMeans class)
kmeans(matrix: DataMatrix, nClusters: number, options: KMeansOptions = {}): { clusters: number[][][], centroids: number[][] } {
validateMatrix(matrix);
const points: number[][] = matrix.data;
// Use the new MiniBatchKMeans class
const kmeans = new KMeans(points, nClusters, options);
const result = kmeans.run();
const centroids = result.clusters.map(c => (c as any).centroid);
const clusters = result.clusters.map(c => (c as any).points);
return { clusters, centroids };
}
// Time helper wrapper functions
getWeekNumber(dateString: string): number {
return getWeekNumber(dateString);
}
getSameWeekDayLastYear(dateString: string): string {
return getSameWeekDayLastYear(dateString);
}
// Retail functions
purchaseRate(productPurchases: number, totalTransactions: number): number {
if (totalTransactions === 0) throw new Error('Total transactions cannot be zero');
return (productPurchases / totalTransactions) * 100;
}
liftValue(jointPurchaseRate: number, productAPurchaseRate: number, productBPurchaseRate: number): number {
const expectedJointRate = productAPurchaseRate * productBPurchaseRate;
if (expectedJointRate === 0) throw new Error('Expected joint rate cannot be zero');
return jointPurchaseRate / expectedJointRate;
}
costRatio(cost: number, salePrice: number): number {
if (salePrice === 0) throw new Error('Sale price cannot be zero');
return cost / salePrice;
}
grossMarginRate(salePrice: number, cost: number): number {
if (salePrice === 0) throw new Error('Sale price cannot be zero');
return (salePrice - cost) / salePrice;
}
averageSpendPerCustomer(totalRevenue: number, numberOfCustomers: number): number {
if (numberOfCustomers === 0) {
throw new Error('Number of customers cannot be zero');
}
return totalRevenue / numberOfCustomers;
}
purchaseIndex(totalItemsSold: number, numberOfCustomers: number): number {
if (numberOfCustomers === 0) {
throw new Error('Number of customers cannot be zero');
}
return (totalItemsSold / numberOfCustomers) * 1000;
}
// ========================================
// Prediction functions
// ========================================
timeSeriesForecast(series: DataSeries, forecastPeriods: number): ForecastResult {
validateSeries(series);
const model = calculateLinearRegression(series.values);
const forecast = generateForecast(model, series.values.length, forecastPeriods);
const predictionIntervals = calculatePredictionIntervals(series.values, model, forecast);
return {
forecast,
predictionIntervals,
modelParameters: {
slope: model.slope,
intercept: model.intercept,
},
};
}
}
// Initialize analytics engine
const analytics = new AnalyticsEngine();
// ======================================== // ========================================
// API ROUTES // API ROUTES
// ======================================== // ========================================
@ -779,6 +479,45 @@ app.post('/api/correlation', (req, res) => {
} }
}); });
/**
* @swagger
* /api/pivot-table:
* post:
* summary: Generate a pivot table from records
* description: Returns a pivot table based on the provided data and options
* tags: [Data Transformation]
* requestBody:
* required: true
* content:
* application/json:
* schema:
* type: object
* properties:
* data:
* type: array
* items:
* type: object
* description: Array of records to pivot
* options:
* $ref: '#/components/schemas/PivotOptions'
* responses:
* '200':
* description: Pivot table generated successfully
* '400':
* description: Invalid input data
*/
app.post('/api/pivot-table', (req, res) => {
try {
const { data, options } = req.body;
// You can pass analytics.mean, analytics.count, etc. as options.aggFunc if needed
const result = pivotTable(data, options);
res.status(200).json({ success: true, data: result });
} catch (error) {
const errorMessage = handleError(error);
res.status(400).json({ success: false, error: errorMessage });
}
});
/** /**
* @swagger * @swagger
* /api/series/moving-average: * /api/series/moving-average:
@ -1150,7 +889,7 @@ app.post('/api/time/same-day-last-year', (req, res) => {
*/ */
app.post('/api/retail/purchase-rate', (req, res) => { app.post('/api/retail/purchase-rate', (req, res) => {
try { try {
const result = analytics.purchaseRate(req.body.productPurchases, req.body.totalTransactions); const result = purchaseRate(req.body.productPurchases, req.body.totalTransactions);
res.status(200).json({ success: true, data: result } as ApiResponse<number>); res.status(200).json({ success: true, data: result } as ApiResponse<number>);
} catch (error) { } catch (error) {
const errorMessage = handleError(error); const errorMessage = handleError(error);
@ -1192,7 +931,7 @@ app.post('/api/retail/purchase-rate', (req, res) => {
*/ */
app.post('/api/retail/lift-value', (req, res) => { app.post('/api/retail/lift-value', (req, res) => {
try { try {
const result = analytics.liftValue(req.body.jointPurchaseRate, req.body.productAPurchaseRate, req.body.productBPurchaseRate); const result = liftValue(req.body.jointPurchaseRate, req.body.productAPurchaseRate, req.body.productBPurchaseRate);
res.status(200).json({ success: true, data: result } as ApiResponse<number>); res.status(200).json({ success: true, data: result } as ApiResponse<number>);
} catch (error) { } catch (error) {
const errorMessage = handleError(error); const errorMessage = handleError(error);
@ -1230,7 +969,7 @@ app.post('/api/retail/lift-value', (req, res) => {
*/ */
app.post('/api/retail/cost-ratio', (req, res) => { app.post('/api/retail/cost-ratio', (req, res) => {
try { try {
const result = analytics.costRatio(req.body.cost, req.body.salePrice); const result = costRatio(req.body.cost, req.body.salePrice);
res.status(200).json({ success: true, data: result } as ApiResponse<number>); res.status(200).json({ success: true, data: result } as ApiResponse<number>);
} catch (error) { } catch (error) {
const errorMessage = handleError(error); const errorMessage = handleError(error);
@ -1268,7 +1007,7 @@ app.post('/api/retail/cost-ratio', (req, res) => {
*/ */
app.post('/api/retail/gross-margin', (req, res) => { app.post('/api/retail/gross-margin', (req, res) => {
try { try {
const result = analytics.grossMarginRate(req.body.salePrice, req.body.cost); const result = grossMarginRate(req.body.salePrice, req.body.cost);
res.status(200).json({ success: true, data: result } as ApiResponse<number>); res.status(200).json({ success: true, data: result } as ApiResponse<number>);
} catch (error) { } catch (error) {
const errorMessage = handleError(error); const errorMessage = handleError(error);
@ -1307,7 +1046,7 @@ app.post('/api/retail/gross-margin', (req, res) => {
app.post('/api/retail/average-spend', (req, res) => { app.post('/api/retail/average-spend', (req, res) => {
try { try {
const { totalRevenue, numberOfCustomers } = req.body; const { totalRevenue, numberOfCustomers } = req.body;
const result = analytics.averageSpendPerCustomer(totalRevenue, numberOfCustomers); const result = averageSpendPerCustomer(totalRevenue, numberOfCustomers);
res.status(200).json({ success: true, data: result } as ApiResponse<number>); res.status(200).json({ success: true, data: result } as ApiResponse<number>);
} catch (error) { } catch (error) {
const errorMessage = handleError(error); const errorMessage = handleError(error);
@ -1346,7 +1085,7 @@ app.post('/api/retail/average-spend', (req, res) => {
app.post('/api/retail/purchase-index', (req, res) => { app.post('/api/retail/purchase-index', (req, res) => {
try { try {
const { totalItemsSold, numberOfCustomers } = req.body; const { totalItemsSold, numberOfCustomers } = req.body;
const result = analytics.purchaseIndex(totalItemsSold, numberOfCustomers); const result = purchaseIndex(totalItemsSold, numberOfCustomers);
res.status(200).json({ success: true, data: result } as ApiResponse<number>); res.status(200).json({ success: true, data: result } as ApiResponse<number>);
} catch (error) { } catch (error) {
const errorMessage = handleError(error); const errorMessage = handleError(error);
@ -1826,6 +1565,29 @@ app.get('/api/kernels/:name', (req, res) => {
* s: * s:
* type: integer * type: integer
* description: The seasonal period length (e.g., 7 for weekly). * description: The seasonal period length (e.g., 7 for weekly).
* PivotOptions:
* type: object
* required:
* - index
* - columns
* - values
* properties:
* index:
* type: array
* items:
* type: string
* description: Keys to use as row labels
* columns:
* type: array
* items:
* type: string
* description: Keys to use as column labels
* values:
* type: string
* description: Key to aggregate
* aggFunc:
* type: string
* description: Aggregation function name (e.g., "sum", "mean", "count")
* ApiResponse: * ApiResponse:
* type: object * type: object
* properties: * properties:

2
analysis_pipelines.ts → services/analysis_pipelines.ts
View file

@ -23,7 +23,7 @@ export interface AutoArimaResult {
P: number; P: number;
D: number; D: number;
Q: number; Q: number;
s: number; // Correctly included s: number;
aic: number; aic: number;
}; };
searchLog: { p: number; d: number; q: number; P: number; D: number; Q: number; s: number; aic: number }[]; searchLog: { p: number; d: number; q: number; P: number; D: number; Q: number; s: number; aic: number }[];

208
services/analytics_engine.ts Normal file
View file

@ -0,0 +1,208 @@
import * as math from 'mathjs';
import * as _ from 'lodash';
import { DataSeries, DataMatrix, Condition, ApiResponse } from '../types/index';
import { RollingWindow } from './rolling_window';
import { KMeans, KMeansOptions } from './kmeans';
import { getWeekNumber, getSameWeekDayLastYear } from './time-helper';
import { calculateLinearRegression, generateForecast, calculatePredictionIntervals, ForecastResult } from './prediction';
export const handleError = (error: unknown): string => {
return error instanceof Error ? error.message : 'Unknown error';
};
export 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');
}
};
export const validateMatrix = (matrix: DataMatrix): void => {
if (!matrix || !Array.isArray(matrix.data) || matrix.data.length === 0) {
throw new Error('Matrix must contain at least one row');
}
};
export class AnalyticsEngine {
private applyConditions(series: DataSeries, conditions: Condition[] = []): number[] {
if (conditions.length === 0) return series.values;
return series.values; // TODO: Implement filtering
}
// Basic statistical functions
unique(series: DataSeries): number[] {
validateSeries(series);
return _.uniq(series.values);
}
mean(series: DataSeries, conditions: Condition[] = []): number {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
if (filteredValues.length === 0) throw new Error('No data points match conditions');
return Number(math.mean(filteredValues));
}
count(series: DataSeries, conditions: Condition[] = []): number {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
if (filteredValues.length === 0) throw new Error('No data points match conditions');
return filteredValues.length;
}
distinctCount(series: DataSeries, conditions: Condition[] = []): number {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
const uniqueValues = _.uniq(filteredValues);
return uniqueValues.length;
}
variance(series: DataSeries, conditions: Condition[] = []): number {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
if (filteredValues.length === 0) throw new Error('No data points match conditions');
return Number(math.variance(filteredValues));
}
standardDeviation(series: DataSeries, conditions: Condition[] = []): number {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
if (filteredValues.length === 0) throw new Error('No data points match conditions');
return Number(math.std(filteredValues));
}
percentile(series: DataSeries, percent: number, ascending: boolean = true, conditions: Condition[] = []): number {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
if (filteredValues.length === 0) throw new Error('No data points match conditions');
const sorted = ascending ? _.sortBy(filteredValues) : _.sortBy(filteredValues).reverse();
const index = (percent / 100) * (sorted.length - 1);
const lower = Math.floor(index);
const upper = Math.ceil(index);
const weight = index % 1;
return sorted[lower] * (1 - weight) + sorted[upper] * weight;
}
median(series: DataSeries, conditions: Condition[] = []): number {
return this.percentile(series, 50, true, conditions);
}
mode(series: DataSeries, conditions: Condition[] = []): number[] {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
const frequency = _.countBy(filteredValues);
const maxFreq = Math.max(...Object.values(frequency));
return Object.keys(frequency)
.filter(key => frequency[key] === maxFreq)
.map(Number);
}
max(series: DataSeries, conditions: Condition[] = []): number {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
if (filteredValues.length === 0) throw new Error('No data points match conditions');
return Math.max(...filteredValues);
}
min(series: DataSeries, conditions: Condition[] = []): number {
validateSeries(series);
const filteredValues = this.applyConditions(series, conditions);
if (filteredValues.length === 0) throw new Error('No data points match conditions');
return Math.min(...filteredValues);
}
correlation(series1: DataSeries, series2: DataSeries): number {
validateSeries(series1);
validateSeries(series2);
if (series1.values.length !== series2.values.length) {
throw new Error('Series must have same length for correlation');
}
const x = series1.values;
const y = series2.values;
const n = x.length;
const sumX = _.sum(x);
const sumY = _.sum(y);
const sumXY = _.sum(x.map((xi, i) => xi * y[i]));
const sumX2 = _.sum(x.map(xi => xi * xi));
const sumY2 = _.sum(y.map(yi => yi * yi));
const numerator = n * sumXY - sumX * sumY;
const denominator = Math.sqrt((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY));
return numerator / denominator;
}
// Rolling window functions
rolling(series: DataSeries, windowSize: number): RollingWindow {
validateSeries(series);
if (windowSize <= 0) {
throw new Error('Window size must be a positive number.');
}
if (series.values.length < windowSize) {
return new RollingWindow([]);
}
const windows: number[][] = [];
for (let i = 0; i <= series.values.length - windowSize; i++) {
const window = series.values.slice(i, i + windowSize);
windows.push(window);
}
return new RollingWindow(windows);
}
movingAverage(series: DataSeries, windowSize: number): number[] {
return this.rolling(series, windowSize).mean();
}
// K-means wrapper (uses imported KMeans class)
kmeans(matrix: DataMatrix, nClusters: number, options: KMeansOptions = {}): { clusters: number[][][], centroids: number[][] } {
validateMatrix(matrix);
const points: number[][] = matrix.data;
// Use the new MiniBatchKMeans class
const kmeans = new KMeans(points, nClusters, options);
const result = kmeans.run();
const centroids = result.clusters.map(c => (c as any).centroid);
const clusters = result.clusters.map(c => (c as any).points);
return { clusters, centroids };
}
// Time helper wrapper functions
getWeekNumber(dateString: string): number {
return getWeekNumber(dateString);
}
getSameWeekDayLastYear(dateString: string): string {
return getSameWeekDayLastYear(dateString);
}
// ========================================
// Prediction functions
// ========================================
timeSeriesForecast(series: DataSeries, forecastPeriods: number): ForecastResult {
validateSeries(series);
const model = calculateLinearRegression(series.values);
const forecast = generateForecast(model, series.values.length, forecastPeriods);
const predictionIntervals = calculatePredictionIntervals(series.values, model, forecast);
return {
forecast,
predictionIntervals,
modelParameters: {
slope: model.slope,
intercept: model.intercept,
},
};
}
}
export const analytics = new AnalyticsEngine();

0
convolution.ts → services/convolution.ts
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0
kmeans.ts → services/kmeans.ts
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36
services/pivot_table.ts Normal file
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import { analytics } from './analytics_engine'; // Import your analytics engine
export interface PivotOptions {
index: string[];
columns: string[];
values: string;
aggFunc?: (items: number[]) => number; // Aggregation function (e.g., analytics.mean)
}
export function pivotTable(
data: Record<string, any>[],
options: PivotOptions
): Record<string, Record<string, number>> {
const { index, columns, values, aggFunc = arr => arr.reduce((a, b) => a + b, 0) } = options;
const cellMap: Record<string, Record<string, number[]>> = {};
data.forEach(row => {
const rowKey = index.map(k => row[k]).join('|');
const colKey = columns.map(k => row[k]).join('|');
if (!cellMap[rowKey]) cellMap[rowKey] = {};
if (!cellMap[rowKey][colKey]) cellMap[rowKey][colKey] = [];
cellMap[rowKey][colKey].push(row[values]);
});
// Apply aggregation function to each cell
const result: Record<string, Record<string, number>> = {};
Object.entries(cellMap).forEach(([rowKey, cols]) => {
result[rowKey] = {};
Object.entries(cols).forEach(([colKey, valuesArr]) => {
result[rowKey][colKey] = aggFunc(valuesArr);
});
});
return result;
}

2
prediction.ts → services/prediction.ts
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@ -48,7 +48,7 @@ export function calculateLinearRegression(yValues: number[]): LinearRegressionMo
// Cast the result of math.sum to a Number // Cast the result of math.sum to a Number
const correlationNumerator = Number(math.sum(xValues.map((x, i) => (x - meanX) * (yValues[i] - meanY)))); const correlationNumerator = Number(math.sum(xValues.map((x, i) => (x - meanX) * (yValues[i] - meanY))));
const correlation = correlationNumerator / ((xValues.length - 1) * stdDevX * stdDevY); const correlation = correlationNumerator / ((xValues.length) * stdDevX * stdDevY);
const slope = correlation * (stdDevY / stdDevX); const slope = correlation * (stdDevY / stdDevX);
const intercept = meanY - slope * meanX; const intercept = meanY - slope * meanX;

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services/retail_metrics.ts Normal file
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export function purchaseIndex(totalItemsSold: number, numberOfCustomers: number): number {
if (numberOfCustomers === 0) {
throw new Error('Number of customers cannot be zero');
}
return (totalItemsSold / numberOfCustomers) * 1000;
}
export function purchaseRate(productPurchases: number, totalTransactions: number): number;
export function purchaseRate(productPurchases: number[], totalTransactions: number[]): number[];
export function purchaseRate(productPurchases: number | number[], totalTransactions: number | number[]): number | number[] {
if (Array.isArray(productPurchases) && Array.isArray(totalTransactions)) {
if (productPurchases.length !== totalTransactions.length) throw new Error('Arrays must have the same length');
return productPurchases.map((pp, i) => purchaseRate(pp, totalTransactions[i]));
}
if (typeof productPurchases === 'number' && typeof totalTransactions === 'number') {
if (totalTransactions === 0) throw new Error('Total transactions cannot be zero');
return (productPurchases / totalTransactions) * 100;
}
throw new Error('Input types must match');
}
export function liftValue(jointPurchaseRate: number, productAPurchaseRate: number, productBPurchaseRate: number): number;
export function liftValue(jointPurchaseRate: number[], productAPurchaseRate: number[], productBPurchaseRate: number[]): number[];
export function liftValue(jointPurchaseRate: number | number[], productAPurchaseRate: number | number[], productBPurchaseRate: number | number[]): number | number[] {
if (Array.isArray(jointPurchaseRate) && Array.isArray(productAPurchaseRate) && Array.isArray(productBPurchaseRate)) {
if (jointPurchaseRate.length !== productAPurchaseRate.length || jointPurchaseRate.length !== productBPurchaseRate.length) throw new Error('Arrays must have the same length');
return jointPurchaseRate.map((jpr, i) => liftValue(jpr, productAPurchaseRate[i], productBPurchaseRate[i]));
}
if (typeof jointPurchaseRate === 'number' && typeof productAPurchaseRate === 'number' && typeof productBPurchaseRate === 'number') {
const expectedJointRate = productAPurchaseRate * productBPurchaseRate;
if (expectedJointRate === 0) throw new Error('Expected joint rate cannot be zero');
return jointPurchaseRate / expectedJointRate;
}
throw new Error('Input types must match');
}
export function costRatio(cost: number, salePrice: number): number;
export function costRatio(cost: number[], salePrice: number[]): number[];
export function costRatio(cost: number | number[], salePrice: number | number[]): number | number[] {
if (Array.isArray(cost) && Array.isArray(salePrice)) {
if (cost.length !== salePrice.length) throw new Error('Arrays must have the same length');
return cost.map((c, i) => costRatio(c, salePrice[i]));
}
if (typeof cost === 'number' && typeof salePrice === 'number') {
if (salePrice === 0) throw new Error('Sale price cannot be zero');
return cost / salePrice;
}
throw new Error('Input types must match');
}
export function grossMarginRate(salePrice: number, cost: number): number;
export function grossMarginRate(salePrice: number[], cost: number[]): number[];
export function grossMarginRate(salePrice: number | number[], cost: number | number[]): number | number[] {
if (Array.isArray(salePrice) && Array.isArray(cost)) {
if (salePrice.length !== cost.length) throw new Error('Arrays must have the same length');
return salePrice.map((sp, i) => grossMarginRate(sp, cost[i]));
}
if (typeof salePrice === 'number' && typeof cost === 'number') {
if (salePrice === 0) throw new Error('Sale price cannot be zero');
return (salePrice - cost) / salePrice;
}
throw new Error('Input types must match');
}
export function averageSpendPerCustomer(totalRevenue: number, numberOfCustomers: number): number;
export function averageSpendPerCustomer(totalRevenue: number[], numberOfCustomers: number[]): number[];
export function averageSpendPerCustomer(totalRevenue: number | number[], numberOfCustomers: number | number[]): number | number[] {
if (Array.isArray(totalRevenue) && Array.isArray(numberOfCustomers)) {
if (totalRevenue.length !== numberOfCustomers.length) throw new Error('Arrays must have the same length');
return totalRevenue.map((tr, i) => averageSpendPerCustomer(tr, numberOfCustomers[i]));
}
if (typeof totalRevenue === 'number' && typeof numberOfCustomers === 'number') {
if (numberOfCustomers === 0) throw new Error('Number of customers cannot be zero');
return totalRevenue / numberOfCustomers;
}
throw new Error('Input types must match');
}

30
services/rolling_window.ts Normal file
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import * as math from 'mathjs';
import * as _ from 'lodash';
export class RollingWindow {
private windows: number[][];
constructor(windows: number[][]) {
this.windows = windows;
}
mean(): number[] {
return this.windows.map(window => Number(math.mean(window)));
}
sum(): number[] {
return this.windows.map(window => _.sum(window));
}
min(): number[] {
return this.windows.map(window => Math.min(...window));
}
max(): number[] {
return this.windows.map(window => Math.max(...window));
}
toArray(): number[][] {
return this.windows;
}
}

0
signal_processing_convolution.ts → services/signal_processing_convolution.ts
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2
time-helper.ts → services/time-helper.ts
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@ -1,5 +1,3 @@
// time-helpers.ts - Date and time utility functions
import { getISOWeek, getISODay, subYears, setISOWeek, setISODay, isValid } from 'date-fns'; import { getISOWeek, getISODay, subYears, setISOWeek, setISODay, isValid } from 'date-fns';
export const getWeekNumber = (dateString: string): number => { export const getWeekNumber = (dateString: string): number => {

0
timeseries.ts → services/timeseries.ts
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21
tests/analyticsEngine.test.ts Normal file
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@ -0,0 +1,21 @@
import { analytics } from '../services/analytics_engine';
describe('AnalyticsEngine', () => {
test('mean returns correct average', () => {
const series = { values: [1, 2, 3, 4, 5] };
const result = analytics.mean(series);
expect(result).toBe(3);
});
test('max returns correct maximum', () => {
const series = { values: [1, 2, 3, 4, 5] };
const result = analytics.max(series);
expect(result).toBe(5);
});
test('min returns correct minimum', () => {
const series = { values: [1, 2, 3, 4, 5] };
const result = analytics.min(series);
expect(result).toBe(1);
});
});

15
tsconfig.json Normal file
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@ -0,0 +1,15 @@
{
"compilerOptions": {
"target": "ES2020",
"module": "commonjs",
"strict": true,
"esModuleInterop": true,
"skipLibCheck": true,
"forceConsistentCasingInFileNames": true,
"resolveJsonModule": true,
"outDir": "./dist",
"rootDir": "./"
},
"include": ["**/*.ts"],
"exclude": ["node_modules", "dist"]
}

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types/index.ts Normal file
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export interface DataSeries {
values: number[];
labels?: string[];
}
export interface DataMatrix {
data: number[][];
columns?: string[];
rows?: string[];
}
export interface Condition {
field: string;
operator: '>' | '<' | '=' | '>=' | '<=' | '!=';
value: number | string;
}
export interface ApiResponse<T> {
success: boolean;
data?: T;
error?: string;
}