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covariates regression extension for TimesFM","maintainers":[{"name":"lambertyan","email":"853901691@qq.com"}],"readme":"# @agentix-e/timesfm-xreg\n\n> Covariate regression extension for TimesFM — Ridge regression + OneHot encoding for exogenous variables.\n\n[![npm](https://img.shields.io/npm/v/@agentix-e/timesfm-xreg?color=blue)](https://www.npmjs.com/package/@agentix-e/timesfm-xreg)\n[![API Docs](https://img.shields.io/badge/docs-TypeDoc-blue)](https://agentix-e.github.io/timesfm-ts/api/modules/timesfm-xreg.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-xreg` extends the TimesFM forecasting pipeline with exogenous covariate support. It provides a scikit-learn-compatible Ridge regression engine and OneHot encoder for categorical variables, used in the `forecastWithCovariates()` workflow.\n\n### Capabilities\n\n- **Dynamic numerical covariates** — Time-varying features (e.g., weather, promotions, events)\n- **Static numerical covariates** — Per-series constant features (e.g., store ID, location)\n- **Categorical covariates** — OneHot-encoded categorical variables with scikit-learn compatibility\n- **Ridge regression** — L2-regularized linear regression for covariate modeling\n- **XReg + TimesFM hybrid** — Combine covariate regression with TimesFM residuals\n\n## Installation\n\n```bash\nnpm install @agentix-e/timesfm-xreg\n```\n\nRequires `@agentix-e/timesfm-core` (peer dependency).\n\n## Quick Start\n\n```typescript\nimport { TimesFMModel, downloadModel, createForecastConfig } from '@agentix-e/timesfm-core';\n\nconst model = await TimesFMModel.fromPretrained({\n  modelPath: await downloadModel(),\n});\nmodel.compile(createForecastConfig({ maxContext: 512, maxHorizon: 128 }));\n\nconst result = await model.forecastWithCovariates({\n  inputs: [new Float32Array(/* historical values */)],\n  dynamicNumericalCovariates: {\n    temperature: [new Float32Array(/* future temperature values */)],\n  },\n  staticNumericalCovariates: {\n    value: [new Float32Array([42])],\n  },\n  xregMode: 'xreg + timesfm',\n});\n```\n\n## API Documentation\n\n📚 **Full API reference**: [agentix-e.github.io/timesfm-ts/api/modules/timesfm-xreg.html](https://agentix-e.github.io/timesfm-ts/api/modules/timesfm-xreg.html)\n\nKey exports:\n\n- `forecastWithCovariates` — Main entry point for covariate-aware forecasting (invoked via `model.forecastWithCovariates()`)\n- `OneHotEncoder` — Scikit-learn-compatible OneHot encoder for categorical covariates\n- `CovariateForecastParams` / `CovariateForecastOutput` / `XRegMode` — Type definitions for covariate forecasting\n\n## License\n\nApache 2.0\n","readmeFilename":"README.md"}