{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "google-research-timesfm--forecast-a-series-zero-shot-with-timesfm",
 "repo": "google-research/timesfm",
 "repo_url": "https://github.com/google-research/timesfm",
 "repo_slug": "google-research-timesfm",
 "title": "Forecast a series without training anything",
 "page_title": "How to forecast a series without training anything with Timesfm",
 "summary": "Load the TimesFM 3.0 checkpoint and predict a horizon with quantiles straight from your history.",
 "who": "An analyst who needs a forecast today and has no model.",
 "problem": "Fitting a model per series is slow and needs tuning.",
 "outcome": "Point forecasts and nine quantiles for each series you pass.",
 "steps": [
  "Install it with `pip install timesfm[torch]`",
  "Create a ModelConfig with checkpoint_path set to google/timesfm-3.0-pytorch and build a TimesFM3Evaluator as in the README",
  "Call forecaster.predict_batch with your list of 1D NumPy arrays, a horizon and return_quantiles=True",
  "Read outputs[0].forecast and outputs[0].quantiles, which have shapes like (12,) and (12, 9)"
 ],
 "agent_prompt": "Using google-research/timesfm, install `pip install timesfm[torch]`, forecast the next 12 steps of each series in <my CSV> with TimesFM3Evaluator and return_quantiles, and write the medians and deciles to a new CSV.",
 "agents": [
  "claude-code",
  "codex",
  "cursor",
  "gemini-cli"
 ],
 "difficulty": "Intermediate",
 "time": "45 minutes",
 "basis": "readme",
 "caveat": "Weights up to version 2.5 remain Apache 2.0 if you need a permissive option.",
 "category": "Data and analytics",
 "tags": [
  "Data and analytics"
 ],
 "lang": "",
 "requirements": [
  "Python with pip, and `pip install timesfm[torch]`, or `pip install timesfm[mlx]` on Apple silicon",
  "A CUDA GPU for the PyTorch examples, which set device to cuda",
  "Your series as NumPy arrays"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [
  {
   "tag": "platform",
   "notice": "TimesFM 3.0 pretrained weights are under a non-commercial license and the README says commercial or production use of self-hosted weights is not permitted."
  }
 ],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/google-research-timesfm/forecast-a-series-zero-shot-with-timesfm/",
 "md": "https://dedupedai.com/use-cases/google-research-timesfm/forecast-a-series-zero-shot-with-timesfm/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/google-research-timesfm--forecast-a-series-zero-shot-with-timesfm.json",
 "api_version": "v1"
}