{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "google-research-timesfm--forecast-with-covariates-using-timesfm",
 "repo": "google-research/timesfm",
 "repo_url": "https://github.com/google-research/timesfm",
 "repo_slug": "google-research-timesfm",
 "title": "Forecast several related series with drivers",
 "page_title": "How to forecast several related series with drivers with Timesfm",
 "summary": "Give TimesFM 3.0 multiple target variates plus past only and past and future covariates in one call.",
 "who": "A planner whose demand depends on price, weather or promotions.",
 "problem": "Univariate models ignore the drivers you already know.",
 "outcome": "A joint forecast across all target variates, with quantiles.",
 "steps": [
  "Install it with `pip install timesfm[torch]`",
  "Shape your targets as a 2D array of (num_variates, context_length) as the README shows",
  "Pass past_only_covariates and past_future_covariates to predict_batch alongside contexts and horizon",
  "Check that outputs[0].forecast has shape (num_variates, horizon) and quantiles adds the nine deciles"
 ],
 "agent_prompt": "With google-research/timesfm, forecast <my three product series> for 24 steps using my known promotions as a past and future covariate, and report the median and the 10th and 90th percentiles.",
 "agents": [
  "claude-code",
  "codex",
  "cursor",
  "gemini-cli"
 ],
 "difficulty": "Intermediate",
 "time": "1 hour",
 "basis": "readme",
 "caveat": "Past and future covariates must cover context plus horizon, as in the README shapes.",
 "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": "The 3.0 weights are non-commercial; production use goes through Google Cloud services per the README."
  }
 ],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/google-research-timesfm/forecast-with-covariates-using-timesfm/",
 "md": "https://dedupedai.com/use-cases/google-research-timesfm/forecast-with-covariates-using-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-with-covariates-using-timesfm.json",
 "api_version": "v1"
}