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* CSV forecasting engine for the TimesFM CLI.
*
* Reads a CSV file, extracts time series, runs TimesFM forecasts,
* and outputs results as CSV or JSON.
*
* @module csv-forecast
*/
import * as fs from 'node:fs';
import { parse } from 'csv-parse/sync';
import { stringify } from 'csv-stringify/sync';
import { TimesFMModel, createForecastConfig } from '@agentix-e/timesfm-core';
// ---------------------------------------------------------------------------
// Logger interface
// ---------------------------------------------------------------------------
/**
* Progress logger for CSV forecasting operations.
*
* Inject a custom implementation to capture progress events
* for logging pipelines or to suppress output in quiet mode.
* Defaults to `console.error` when omitted.
*/
export interface CSVForecastLogger {
info(msg: string): void;
error(msg: string): void;
}
const defaultLogger: CSVForecastLogger = {
info(msg: string) {
console.error(msg);
},
error(msg: string) {
console.error(msg);
},
};
// ---------------------------------------------------------------------------
// Types
// ---------------------------------------------------------------------------
export interface CSVForecastOptions {
inputPath: string;
horizon: number;
modelPath: string;
dateCol: string;
valueCols?: string[];
outputPath?: string;
outputFormat: 'csv' | 'json';
maxContext: number;
normalizeInputs: boolean;
forceFlipInvariance: boolean;
inferIsPositive: boolean;
fixQuantileCrossing: boolean;
useContinuousQuantileHead: boolean;
/** Optional progress logger (defaults to console.error). */
logger?: CSVForecastLogger;
}
interface ParsedCSV {
dates: string[];
series: Map<string, Float32Array>;
}
// ---------------------------------------------------------------------------
// CSV parsing
// ---------------------------------------------------------------------------
export function parseCSVData(filePath: string, dateCol: string, valueCols?: string[]): ParsedCSV {
const raw = fs.readFileSync(filePath, 'utf-8');
const records: Record<string, string>[] = parse(raw, {
columns: true,
skip_empty_lines: true,
trim: true,
});
if (records.length === 0) {
throw new Error(`Empty CSV file: ${filePath}`);
}
// Identify columns
const allCols = Object.keys(records[0]);
const numericCols = valueCols ?? allCols.filter((c) => c !== dateCol);
// Extract dates
const dates = records.map((r) => r[dateCol] ?? '');
// Extract series
const series = new Map<string, Float32Array>();
for (const col of numericCols) {
const values: number[] = [];
for (const record of records) {
const v = parseFloat(record[col]);
values.push(Number.isFinite(v) ? v : NaN);
}
series.set(col, removeTrailingNaN(new Float32Array(values)));
}
return { dates, series };
}
export function removeTrailingNaN(arr: Float32Array): Float32Array {
let end = arr.length;
while (end > 0 && Number.isNaN(arr[end - 1])) end--;
return arr.slice(0, end);
}
// ---------------------------------------------------------------------------
// Main forecast function
// ---------------------------------------------------------------------------
export async function csvForecast(options: CSVForecastOptions): Promise<void> {
const log = options.logger ?? defaultLogger;
// Parse input
const { series } = parseCSVData(options.inputPath, options.dateCol, options.valueCols);
log.info(`Loaded ${series.size} series from ${options.inputPath}`);
// Create model
const model = await TimesFMModel.fromPretrained({
modelPath: options.modelPath,
});
// Compile
const fc = createForecastConfig({
maxContext: options.maxContext,
maxHorizon: options.horizon,
normalizeInputs: options.normalizeInputs,
forceFlipInvariance: options.forceFlipInvariance,
inferIsPositive: options.inferIsPositive,
fixQuantileCrossing: options.fixQuantileCrossing,
useContinuousQuantileHead: options.useContinuousQuantileHead,
});
model.compile(fc);
log.info(`Compiled model with maxContext=${fc.maxContext}, maxHorizon=${fc.maxHorizon}`);
// Forecast
const inputList: Float32Array[] = [];
const seriesNames: string[] = [];
for (const [name, data] of series) {
inputList.push(data);
seriesNames.push(name);
}
log.info(`Forecasting ${inputList.length} series for ${options.horizon} steps...`);
const result = await model.forecast(options.horizon, inputList);
// Output
if (options.outputFormat === 'json') {
outputJSON(result, seriesNames, options, log);
} else {
outputCSV(result, seriesNames, options, log);
}
await model.dispose();
log.info('Done.');
}
// ---------------------------------------------------------------------------
// Output formatters
// ---------------------------------------------------------------------------
export function outputCSV(
result: { pointForecast: Float32Array[]; quantileForecast: Float32Array[][] },
seriesNames: string[],
options: CSVForecastOptions,
log?: CSVForecastLogger,
): void {
const logFn = log ?? defaultLogger;
const rows: Record<string, string | number>[] = [];
for (let s = 0; s < result.pointForecast.length; s++) {
const pf = result.pointForecast[s];
const qf = result.quantileForecast[s];
for (let h = 0; h < pf.length; h++) {
rows.push({
series_id: seriesNames[s],
horizon_step: h + 1,
point_forecast: pf[h],
q10: qf[1][h],
q50: qf[5][h],
q90: qf[9][h],
});
}
}
const csv = stringify(rows, { header: true });
if (options.outputPath) {
fs.writeFileSync(options.outputPath, csv);
logFn.info(`Wrote ${rows.length} rows to ${options.outputPath}`);
} else {
process.stdout.write(csv);
}
}
export function outputJSON(
result: { pointForecast: Float32Array[]; quantileForecast: Float32Array[][] },
seriesNames: string[],
options: CSVForecastOptions,
log?: CSVForecastLogger,
): void {
const logFn = log ?? defaultLogger;
const output: Record<string, unknown> = {
model: 'timesfm-2.5',
horizon: options.horizon,
series: {},
};
const seriesOut = output.series as Record<string, unknown>;
for (let s = 0; s < result.pointForecast.length; s++) {
seriesOut[seriesNames[s]] = {
point_forecast: Array.from(result.pointForecast[s]),
lower_80: Array.from(result.quantileForecast[s][1]), // q10
upper_80: Array.from(result.quantileForecast[s][9]), // q90
quantiles: {
q10: Array.from(result.quantileForecast[s][1]),
q20: Array.from(result.quantileForecast[s][2]),
q30: Array.from(result.quantileForecast[s][3]),
q40: Array.from(result.quantileForecast[s][4]),
q50: Array.from(result.quantileForecast[s][5]),
q60: Array.from(result.quantileForecast[s][6]),
q70: Array.from(result.quantileForecast[s][7]),
q80: Array.from(result.quantileForecast[s][8]),
q90: Array.from(result.quantileForecast[s][9]),
},
};
}
const json = JSON.stringify(output, null, 2);
if (options.outputPath) {
fs.writeFileSync(options.outputPath, json);
logFn.info(`Wrote JSON to ${options.outputPath}`);
} else {
process.stdout.write(json);
}
}
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