timesfm-2.5-200m-pytorch
google/timesfm-2.5-200m-pytorch
Tracked by OpenModelStats since August 24, 2026View on Hugging Face ↗
- Downloads 30DRolling recent-window download count as reported by the source platform.
- 0
- Total downloadsCumulative all-time download counter reported by the source platform.
- 6
- Gained 7DIncrease in cumulative total downloads between OpenModelStats' own observations seven days apart.
- —
- Gained 30DIncrease in cumulative total downloads between OpenModelStats' own observations roughly thirty days apart.
- —
- LikesCommunity likes reported by the source platform.
- 257
- Rank · tracked modelsPosition among all eligible models tracked by OpenModelStats, ordered by recent downloads. #83 among tracked Time Series Forecasting models.
- #32,983
- 29860(down 29860 places)this week
- Parameters
- 231M
- Last source update
- Oct 2, 2025
- It moved from #7 to #83 among tracked Time Series Forecasting models this week.
Downloads over time
Cumulative total downloads observed by OpenModelStatsNot enough tracked history yet for a chart.
OpenModelStats began tracking this model on 2026-08-24. Charts appear as daily observations accumulate.
Likes over time
Not enough tracked history yet for a chart.
OpenModelStats began tracking this model on 2026-08-24. Charts appear as daily observations accumulate.
Rank among tracked models
Lower is betterNot enough tracked history yet for a chart.
OpenModelStats began tracking this model on 2026-08-24. Charts appear as daily observations accumulate.
Overview
timesfm-2.5-200m-pytorch is a time series forecasting model published by google. OpenModelStats has tracked the model since Aug 24, 2026, most recently observing it 44 min ago.
- Published
- Sep 2, 2025
- Last updated
- Oct 2, 2025
- Library
- timesfm
- License
- apache-2.0
- Task
- Time Series Forecasting
- Parameters
- 231,289,280
- Spaces
- —
- Derivative models
- —
- Velocity 7D/day
- —
timesfmsafetensorstime-series-forecastingarxiv:2310.10688license:apache-2.0region:us
Publisher
googleView publisher statistics →Similar models
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