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time-series forecasting for the browser — WebAssembly inference via onnxruntime-web","maintainers":[{"name":"lambertyan","email":"853901691@qq.com"}],"readme":"# @agentix-e/timesfm-web\n\n> Browser inference engine for TimesFM — zero-shot forecasting via WebAssembly, WebGPU, or WebGL.\n\n[![npm](https://img.shields.io/npm/v/@agentix-e/timesfm-web?color=orange)](https://www.npmjs.com/package/@agentix-e/timesfm-web)\n[![API Docs](https://img.shields.io/badge/docs-TypeDoc-blue)](https://agentix-e.github.io/timesfm-ts/api/modules/timesfm-web.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-web` enables TimesFM inference directly in the browser using `onnxruntime-web`. It supports multiple backends (WASM, WebGPU, WebGL) and loads the 885 MB ONNX model via `fetch()` with streaming.\n\n### Backend Support\n\n| Backend | Browser Support        | Performance |\n| ------- | ---------------------- | ----------- |\n| WebGPU  | Chrome 113+, Edge 113+ | 🚀 Fastest  |\n| WASM    | All modern browsers    | ✅ Default  |\n| WebGL   | Fallback               | ⚠️ Slow     |\n\n> **Memory note**: The 885 MB model must fit in browser WASM memory (~4 GB limit).\n\n## Installation\n\n```bash\nnpm install @agentix-e/timesfm-web onnxruntime-web\n```\n\n`onnxruntime-web` is a peer dependency — install it alongside.\n\n## Quick Start\n\n```typescript\nimport { TimesFMModel, createForecastConfig, TIMESFM_25_CONFIG } from '@agentix-e/timesfm-core';\nimport { TimesFMWebInferenceEngine, loadModelFromUrl } from '@agentix-e/timesfm-web';\n\n// 1. Download the model via fetch() with progress tracking\nconst { buffer } = await loadModelFromUrl('https://cdn.example.com/timesfm-2.5.onnx', {\n  onProgress: (received, total) => console.log(`${received}/${total}`),\n});\n\n// 2. Create web engine and load model\nconst engine = new TimesFMWebInferenceEngine(TIMESFM_25_CONFIG);\nawait engine.load(buffer);\n\n// 3. Create model with injected web engine (DI pattern)\nconst model = await TimesFMModel.fromPretrained({\n  modelPath: '/models/timesfm-2.5.onnx',\n  engine,\n});\n\n// 4. Compile and forecast\nmodel.compile(createForecastConfig({ maxContext: 1024, maxHorizon: 256 }));\nconst result = await model.forecast(24, [\n  new Float32Array([\n    /* historical values */\n  ]),\n]);\n\nconsole.log(result.pointForecast); // Shape: [1, 24]\nconsole.log(result.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-web.html](https://agentix-e.github.io/timesfm-ts/api/modules/timesfm-web.html)\n\nKey exports:\n\n- `TimesFMWebInferenceEngine` — Browser inference engine implementing `IInferenceEngine` (WebGPU / WASM / WebGL)\n- `loadModelFromUrl` — Fetch-based model loader with streaming progress callback\n- `checkModelAvailability` — HEAD request check for model availability\n- `WebEngineLogger` — Custom logger interface for browser engine diagnostics\n\n## License\n\nApache 2.0\n","readmeFilename":"README.md"}