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* TimesFM Model — the main public API.
*
* Usage:
*
* ```typescript
* import { TimesFMModel } from '@agentix-e/timesfm-core';
* import { createForecastConfig } from '@agentix-e/timesfm-core';
*
* // Always requires a valid ONNX model path
* const model = await TimesFMModel.fromPretrained({
* modelPath: './models/timesfm-2.5.onnx',
* });
*
* model.compile(createForecastConfig({
* maxContext: 1024,
* maxHorizon: 256,
* }));
*
* const { pointForecast, quantileForecast } = await model.forecast(
* 24,
* [new Float32Array([1, 2, 3, ...])],
* );
*
* await model.dispose();
* ```
*/
import {
TIMESFM_25_CONFIG,
type IInferenceEngine,
type ForecastConfig,
type ModelConfig,
type ModelLoadOptions,
type ForecastOutput,
type ForecastCallOptions,
type ITimesFMModel,
type CovariateForecastParams,
type CovariateForecastOutput,
} from './types';
import { ModelNotFoundError, ModelNotCompiledError, HorizonExceededError } from './errors';
import { validateAndNormalizeConfig } from './config';
import { TimesFMInferenceEngine } from './inference/onnx-engine';
import { preprocess } from './preprocessor';
import { decode } from './inference/decode-loop';
import { postProcess } from './postprocessor';
import { resolveModelConfig } from './model-descriptor';
import { computeStats } from './utils/stats';
import { allNonNegative } from './utils/tensor-utils';
// Cached dynamic import for optional @agentix-e/timesfm-xreg peer dependency.
//
// Uses a Promise-based singleton pattern to handle concurrent calls safely:
// • First call initializes the promise (only one import() in flight)
// • Subsequent calls wait on the same promise
// • On failure, the promise is reset so the next call retries
// • After success, _xregModule is set so subsequent calls resolve instantly
//
// eslint-disable-next-line @typescript-eslint/consistent-type-imports
type XRegModule = typeof import('@agentix-e/timesfm-xreg');
let _xregModule: XRegModule | null = null;
let _xregModulePromise: Promise<XRegModule> | null = null;
let _xregImportFn: (spec: string) => Promise<unknown> = (spec: string) => import(spec);
/**
* Override the import function used to load @agentix-e/timesfm-xreg.
*
* **Test-only seam** — NOT part of the public API. Exported only so tests
* can exercise the dynamic-import failure path without vitest mocking.
*
* Reset with `null` to restore default behaviour.
*
* @internal
*/
export function __test_setXregImport(fn: ((spec: string) => Promise<unknown>) | null): void {
_xregImportFn = fn ?? ((spec: string) => import(spec));
_xregModule = null; // Invalidate cache so next call uses the new fn
_xregModulePromise = null; // Invalidate pending promise
}
// ---------------------------------------------------------------------------
// TimesFMModel
// ---------------------------------------------------------------------------
export class TimesFMModel implements ITimesFMModel {
private _engine: IInferenceEngine;
private _config: ModelConfig;
private _forecastConfig: ForecastConfig | null = null;
private _globalBatchSize: number = 0;
private _compiled: boolean = false;
private constructor(engine: IInferenceEngine, config: ModelConfig) {
this._engine = engine;
this._config = config;
}
// -----------------------------------------------------------------------
// Factory
// -----------------------------------------------------------------------
/**
* Create a TimesFM model from a pretrained ONNX checkpoint.
*
* The model architecture is resolved from the `model-descriptor.json`
* co-located with the ONNX file. When no descriptor is found, the
* engine falls back to the canonical TimesFM 2.5 200M config.
*
* @param options.modelPath Path to the TimesFM ONNX model file. Required.
* @param options.executionProvider 'cpu' (default), 'cuda', or 'dml'.
*
* @throws {Error} if modelPath is not provided or the file doesn't exist.
*/
static async fromPretrained(options: ModelLoadOptions): Promise<TimesFMModel> {
if (!options.modelPath) {
throw new ModelNotFoundError(
'modelPath is required. Provide the path to a TimesFM ONNX model file.\n' +
'To obtain a model:\n' +
' 1. Run: python scripts/export-onnx.py (if you have the TimesFM PyTorch model)\n' +
' 2. Or download a pre-converted ONNX model\n' +
'See docs/GETTING-STARTED.md for details.',
);
}
// Resolve architecture from model-descriptor.json (single source of truth).
// Falls back to TIMESFM_25_CONFIG when no descriptor is present, maintaining
// backward compatibility with bare .onnx files.
const { config: mc, descriptor } = await resolveModelConfig(
options.modelPath,
TIMESFM_25_CONFIG,
);
if (descriptor) {
// Log model identity for traceability
// eslint-disable-next-line no-console
console.log(
`[TimesFM] Loaded descriptor: v${descriptor.model.version}-${descriptor.model.variant}` +
` (schema ${descriptor.schema}, hf_rev ${descriptor.model.hf_revision}), ` +
`${descriptor.onnx.size_bytes > 0 ? (descriptor.onnx.size_bytes / 1024 ** 2).toFixed(0) + ' MB' : ''}`,
);
}
const engine =
options.engine ??
new TimesFMInferenceEngine(mc, {
executionProvider: options.executionProvider,
intraOpNumThreads: options.intraOpNumThreads,
});
// Only load if the engine is not already loaded (external engine injection)
if (!engine.isLoaded()) {
await engine.load(options.modelPath, { skipWarmup: options.skipWarmup });
}
return new TimesFMModel(engine, mc);
}
// -----------------------------------------------------------------------
// Compile
// -----------------------------------------------------------------------
/**
* Compile the model with the given forecast configuration.
*
* This must be called before `forecast()`. It validates and normalises
* the config and sets up batch-size calculations.
*
* @returns `this` for method chaining.
* @throws {RangeError} if context + horizon exceeds the model limit.
*/
compile(fc: ForecastConfig): this {
const normalized = validateAndNormalizeConfig(fc, this._config);
this._forecastConfig = normalized;
this._globalBatchSize = normalized.perCoreBatchSize;
this._compiled = true;
return this;
}
get isCompiled(): boolean {
return this._compiled;
}
get forecastConfig(): ForecastConfig | null {
return this._forecastConfig;
}
// -----------------------------------------------------------------------
// Forecast
// -----------------------------------------------------------------------
/**
* Forecast a batch of univariate time series.
*
* @param horizon Number of future time points to forecast.
* @param inputs List of 1-D time series. Each can have different length.
* May contain NaN values (leading NaNs are stripped,
* internal NaNs are linearly interpolated).
* @param options Optional AbortSignal and progress callback.
*
* @returns Point forecasts + quantile forecasts.
*
* @throws {Error} if the model has not been compiled.
* @throws {DOMException} AbortError if the operation was cancelled.
*/
async forecast(
horizon: number,
inputs: Float32Array[],
options?: ForecastCallOptions,
): Promise<ForecastOutput> {
if (!this._compiled || !this._forecastConfig) {
throw new ModelNotCompiledError('Model not compiled. Call compile() before forecast().');
}
// Per-call config overrides (used internally by XReg covariate workflows
// for returnBackcast without mutating global state). The stored config
// on the model instance is never modified.
const fc: ForecastConfig = options?.configOverrides
? { ...this._forecastConfig, ...options.configOverrides }
: this._forecastConfig;
const signal = options?.signal;
const onProgress = options?.onProgress;
// Check for early abort
signal?.throwIfAborted();
if (horizon > fc.maxHorizon) {
throw new HorizonExceededError(`Horizon ${horizon} exceeds maxHorizon ${fc.maxHorizon}.`);
}
// Pad batch to globalBatchSize — synthetic zero-padding series
// are appended to align the input count to perCoreBatchSize.
// These are tracked separately so we can skip them in the decode
// pipeline and trim the output to numInputs, avoiding wasted ONNX
// inference on dummy data (~12% compute saving at typical batch sizes).
const paddedInputs = [...inputs];
const numInputs = inputs.length;
const remainder = numInputs % this._globalBatchSize;
let padCount = 0;
if (remainder !== 0) {
padCount = this._globalBatchSize - remainder;
for (let i = 0; i < padCount; i++) {
paddedInputs.push(new Float32Array([0, 0, 0]));
}
}
// Input z-score normalization
let normalizedInputs = paddedInputs;
const inputStats: { mu: number; sigma: number }[] = [];
/** Per-series flag: true if raw (pre-normalized) input is all ≥ 0. */
const isPositiveFlags: boolean[] = [];
if (fc.normalizeInputs) {
normalizedInputs = paddedInputs.map((arr) => {
const { mean: mu, std: sigma } = computeStats(arr);
const safeSigma = sigma < 1e-6 ? 1 : sigma;
const isNonNeg = allNonNegative(arr);
inputStats.push({ mu, sigma: safeSigma });
isPositiveFlags.push(isNonNeg);
const result = new Float32Array(arr.length);
for (let i = 0; i < arr.length; i++) {
result[i] = Number.isFinite(arr[i]) ? (arr[i] - mu) / safeSigma : 0;
}
return result;
});
} else {
// When not normalizing, still compute positivity flags
for (const arr of paddedInputs) {
inputStats.push({ mu: 0, sigma: 1 });
isPositiveFlags.push(allNonNegative(arr));
}
}
const allPointForecasts: Float32Array[] = [];
const allQuantileForecasts: Float32Array[][] = [];
const allBackcasts: Float32Array[] = [];
const batchSize = this._globalBatchSize;
const numBatches = Math.ceil(normalizedInputs.length / batchSize);
for (let bi = 0; bi < numBatches; bi++) {
// Abort check at batch boundary
signal?.throwIfAborted();
const batchStart = bi * batchSize;
const batchInputs = normalizedInputs.slice(batchStart, batchStart + batchSize);
onProgress?.({
phase: 'preprocess',
step: bi + 1,
total: numBatches,
batchIndex: bi,
totalBatches: numBatches,
});
const preprocessed = preprocess(batchInputs, fc, this._config);
onProgress?.({
phase: 'prefill',
step: bi + 1,
total: numBatches,
batchIndex: bi,
totalBatches: numBatches,
});
// ── Decode (main path, and optionally flip path in parallel) ──────
let decodeResult: Awaited<ReturnType<typeof decode>>;
let flipResult: Awaited<ReturnType<typeof decode>> | null = null;
if (fc.forceFlipInvariance) {
// Pre-build the negated flip inputs while the main decode runs.
// Then launch both decode() calls concurrently via Promise.all
// so the flip path doesn't add serial latency. ONNX Runtime
// sessions are safe for concurrent calls in the Node.js event loop.
const negInputs = batchInputs.map((arr) => {
const neg = new Float32Array(arr.length);
for (let i = 0; i < arr.length; i++) neg[i] = -arr[i];
return neg;
});
const flipPre = preprocess(negInputs, fc, this._config);
signal?.throwIfAborted();
onProgress?.({
phase: 'flip',
step: bi + 1,
total: numBatches,
batchIndex: bi,
totalBatches: numBatches,
});
// Parallel: main + flip decode run concurrently
[decodeResult, flipResult] = await Promise.all([
decode(
this._engine,
preprocessed.patchedInputs,
preprocessed.patchedMasks,
preprocessed.contextMu,
preprocessed.contextSigma,
preprocessed.lastStats,
fc.maxHorizon,
fc,
this._config,
signal,
),
decode(
this._engine,
flipPre.patchedInputs,
flipPre.patchedMasks,
flipPre.contextMu,
flipPre.contextSigma,
flipPre.lastStats,
fc.maxHorizon,
fc,
this._config,
signal,
),
]);
} else {
decodeResult = await decode(
this._engine,
preprocessed.patchedInputs,
preprocessed.patchedMasks,
preprocessed.contextMu,
preprocessed.contextSigma,
preprocessed.lastStats,
fc.maxHorizon,
fc,
this._config,
signal,
);
}
onProgress?.({
phase: 'postprocess',
step: bi + 1,
total: numBatches,
batchIndex: bi,
totalBatches: numBatches,
});
const batchInputStats = fc.normalizeInputs
? inputStats.slice(batchStart, batchStart + batchSize)
: null;
const batchIsPositive = isPositiveFlags.slice(batchStart, batchStart + batchSize);
const output = postProcess(
decodeResult,
horizon,
fc,
this._config,
batchInputStats,
flipResult,
batchIsPositive,
);
allPointForecasts.push(...output.pointForecast);
allQuantileForecasts.push(...output.quantileForecast);
if (output.backcast) {
allBackcasts.push(...output.backcast);
}
}
return {
pointForecast: allPointForecasts.slice(0, numInputs),
quantileForecast: allQuantileForecasts.slice(0, numInputs),
backcast: allBackcasts.length > 0 ? allBackcasts.slice(0, numInputs) : undefined,
};
}
/**
* Forecast with exogenous covariates via {@link @agentix-e/timesfm-xreg}.
*
* Internally caches the dynamic import of the optional peer dependency
* so that repeated calls avoid re-resolution overhead.
*
* Concurrent safety: uses a Promise-based singleton pattern so that
* multiple concurrent first-time calls share a single import() and
* import failures are retried on subsequent calls.
*/
async forecastWithCovariates(params: CovariateForecastParams): Promise<CovariateForecastOutput> {
// Fast path: module is already loaded (after first successful import).
if (_xregModule) {
return await _xregModule.forecastWithCovariates(this, params);
}
// Init path: start (or join) a single inflight import promise.
//
// Wrap in a helper that handles both async (rejected promise) and
// sync (thrown error) failures from the import function — the
// test seam __test_setXregImport may provide a function that throws
// synchronously.
if (!_xregModulePromise) {
const startImport = async (): Promise<XRegModule> => {
try {
const mod = (await _xregImportFn('@agentix-e/timesfm-xreg')) as XRegModule;
_xregModule = mod;
return mod;
} catch {
_xregModulePromise = null;
throw new Error(
'forecastWithCovariates requires @agentix-e/timesfm-xreg.\n' +
'Install it: npm install @agentix-e/timesfm-xreg',
);
}
};
_xregModulePromise = startImport();
}
// _xregModulePromise is guaranteed non-null after the init block above.
// The Promise<XRegModule> | null type on the module-level var prevents
// TypeScript from narrowing — we use a type assertion here because the
// runtime guarantee is structurally enforced by the code above.
const promise = _xregModulePromise as Promise<XRegModule>;
return (await promise).forecastWithCovariates(this, params);
}
async dispose(): Promise<void> {
await this._engine.dispose();
this._compiled = false;
this._forecastConfig = null;
}
get engine(): IInferenceEngine {
return this._engine;
}
get modelConfig(): ModelConfig {
return this._config;
}
get globalBatchSize(): number {
return this._globalBatchSize;
}
}
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