MODEL PROFILE

timesfm-2.0-500m-pytorch

Google · timesfm-2.0-500m-pytorch

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. * Paper: A decoder-only foundation model for time-series forecasting, ICML 2024. * Google Research blog * GitHub repo - It performs univariate time series forecasting for context lengths up to 2048 time points and any horizon lengths, with an optional frequency indicator. Note that it can go even beyond 2048 context even though it was trained with that as the maximum context. - It focuses on point forecasts. We experimentally offer 10 quantile heads but they have not been calibrated after pretraining. - It ideally requires the context to be contiguous (i.e. no "holes"), and the context and the horizon to be of the same frequency. In case there are nans we fill in the missing values with linear interpolation before calling the model.

Tabular & time serieslicense:apache-2.0
Context windowNot disclosed
Input → Output待核验 → 待核验
Model weightsNot verified
Profile updated2026/11/2 19:16:22
OVERVIEW

Model overview

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. * Paper: A decoder-only foundation model for time-series forecasting, ICML 2024. * Google Research blog * GitHub repo - It performs univariate time series forecasting for context lengths up to 2048 time points and any horizon lengths, with an optional frequency indicator. Note that it can go even beyond 2048 context even though it was trained with that as the maximum context. - It focuses on point forecasts. We experimentally offer 10 quantile heads but they have not been calibrated after pretraining. - It ideally requires the context to be contiguous (i.e. no "holes"), and the context and the horizon to be of the same frequency. In case there are nans we fill in the missing values with linear interpolation before calling the model.

Key features

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Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。