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inference engine for TimesFM — zero-shot time series forecasting","maintainers":[{"name":"lambertyan","email":"853901691@qq.com"}],"readme":"# @agentix-e/timesfm-core\n\n> Core inference engine for TimesFM — zero-shot time series forecasting powered by ONNX Runtime.\n\n[![npm](https://img.shields.io/npm/v/@agentix-e/timesfm-core?color=blue)](https://www.npmjs.com/package/@agentix-e/timesfm-core)\n[![API Docs](https://img.shields.io/badge/docs-TypeDoc-blue)](https://agentix-e.github.io/timesfm-ts/api/modules/timesfm-core.html)\n[![Benchmark](https://img.shields.io/badge/benchmark-latest-blue)](https://agentix-e.github.io/timesfm-ts/benchmark/)\n[![Coverage](https://img.shields.io/badge/coverage-report-blue)](https://agentix-e.github.io/timesfm-ts/coverage/)\n\n## Overview\n\n`@agentix-e/timesfm-core` is the heart of timesfm-ts — a production-grade Node.js/TypeScript implementation of Google Research's TimesFM 2.5 (200M parameter decoder-only transformer). It provides zero-shot univariate time-series forecasting with calibrated prediction intervals, no training required.\n\n### Architecture\n\n```\nRaw Series → [NaN Handler] → [Pad/Truncate] → [Patch Split] → [RevIN Norm]\n→ [ONNX Runtime] → [RevIN Denorm] → [Flip Invariance] → [Quantile Calibration]\n→ Forecasts\n```\n\n## Installation\n\n```bash\nnpm install @agentix-e/timesfm-core\n```\n\nRequires Node.js ≥ 22.\n\n## Quick Start\n\n```typescript\nimport { TimesFMModel, downloadModel, createForecastConfig } from '@agentix-e/timesfm-core';\n\n// Auto-download model (~885 MB, first time only, cached thereafter)\nconst modelPath = await downloadModel();\n\nconst model = await TimesFMModel.fromPretrained({ modelPath });\nmodel.compile(createForecastConfig({ maxContext: 1024, maxHorizon: 256 }));\n\nconst { pointForecast, quantileForecast } = await model.forecast(24, [\n  new Float32Array([1, 2, 3 /* ... */]),\n]);\n\nconsole.log(pointForecast); // Shape: [1, 24]\nconsole.log(quantileForecast); // Shape: [1, 24, 10]\n\nawait model.dispose();\n```\n\n## API Documentation\n\n📚 **Full API reference**: [agentix-e.github.io/timesfm-ts/api/modules/timesfm-core.html](https://agentix-e.github.io/timesfm-ts/api/modules/timesfm-core.html)\n\nKey exports:\n\n- `TimesFMModel` — Main model class (`fromPretrained`, `compile`, `forecast`, `forecastWithCovariates`)\n- `IInferenceEngine`, `RawModelOutput` — Engine abstraction (implementations: `@agentix-e/timesfm-node`, `@agentix-e/timesfm-web`)\n- `downloadModel` / `defaultModelPath` / `isModelCached` — Model download & cache management\n- `createForecastConfig` / `validateAndNormalizeConfig` — Configuration builder\n- `preprocess` / `postProcess` — Preprocessing & postprocessing pipelines\n- `decode` — Autoregressive decode loop\n- `mae`, `rmse`, `mape`, `smape`, `mase`, `r2Score` — Evaluation metrics\n- Utility exports: `cleanSeries`, `stripLeadingNaNs`, `linearInterpolateNaNs`, `computeStats`, `revin`, `revinBatch`\n\n## Model Download\n\n```typescript\nimport { downloadModel } from '@agentix-e/timesfm-core';\n\n// Default: ~/.cache/timesfm-ts/timesfm-2.5.onnx\nconst path = await downloadModel();\n\n// With proxy (corporate network)\nconst path = await downloadModel({\n  proxy: { url: 'http://proxy.company.com:8080', username: 'user', password: 'pass' },\n  onProgress: (received, total, speed) => console.log(`${received}/${total} MB @ ${speed} MB/s`),\n});\n```\n\nProxy can also be configured via environment variables:\n\n- `TIMESFM_PROXY_URL` / `TIMESFM_PROXY_USERNAME` / `TIMESFM_PROXY_PASSWORD`\n- `TIMESFM_PROXY_PASSWORD_FILE` — read password from a file (Docker/K8s secrets)\n- Standard `HTTPS_PROXY` / `HTTP_PROXY`\n\n## License\n\nApache 2.0\n","readmeFilename":"README.md"}