google-research/timesfm

Health v2 · maintenance only

Adoption not part of the score

28273 stars · 2761 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

TimesFM is a pretrained decoder-only foundation model from Google Research for time-series forecasting, offering zero-shot forecasts with up to 16k context length and optional quantile heads. It ships as a Python package with PyTorch and Flax implementations, covariate support, and LoRA fine-tuning examples.

Use cases

• forecast demand from historical sales time series
• zero-shot time-series forecasting without training a model
• predict future values of a metric given past observations
• fine-tune a pretrained forecasting model with LoRA
• generate quantile forecasts for uncertainty estimation
• forecast financial or retail time series in Python

When to choose

• you need strong out-of-the-box forecasts without per-dataset training
• you want long-context forecasting up to 16k time points
• you need probabilistic quantile forecasts
• you want a lightweight 200M-parameter model for inference

When to avoid

• you need fully interpretable classical methods like ARIMA or ETS
• you require real-time streaming forecasts at very low latency
• you need multivariate modeling with complex cross-series dependencies
• you cannot use GPU acceleration for reasonable inference speed

Facets

library · maturity active

machine-learning llm-inference llm-training data-science machine-learning time-series data-science large-language-models python cross-platform time-series-forecasting foundation-model zero-shot-forecasting decoder-only quantile-forecasting pretrained-model huggingface lora-finetuning gpu

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