All 25 Dependencies
Every package timesfm depends on, ranked by repo health score.
TimesFM (Time Series Foundation Model) is a production-grade, pretrained foundation model developed by Google Research that brings the zero-shot transfer paradigm from NLP to time series forecasting. Rather than requiring per-dataset model training, TimesFM accepts any univariate time series as input and immediately returns point forecasts alongside calibrated probabilistic quantile intervals — covering the 10th through 90th percentiles — without any fine-tuning.
Version 2.5 of the model uses 200 million parameters and supports context windows up to 16,384 time steps, a dramatic increase from the 2,048-step limit of earlier versions. The model natively handles variable-length inputs, NaN imputation, and batch inference across hundreds of series simultaneously. An optional quantile head (30M parameters) enables continuous quantile forecasting up to a 1,000-step horizon, and a covariate extension (XReg) lets users layer in dynamic or static exogenous signals — such as promotional calendars, day-of-week indicators, or external pricing data — on top of the base model.
TimesFM ships as an installable Python package with separate extras for PyTorch and Flax (JAX) backends, enabling deployment on CPU, CUDA GPU, TPU, or Apple Silicon. The model weights are distributed through Hugging Face Hub, and the package integrates tightly with the HuggingFace `from_pretrained` pattern for one-line model loading. Beyond the open-source library, Google ships TimesFM commercially through BigQuery ML, Google Sheets Connected Sheets, and Vertex AI Model Garden, meaning teams that want SQL-level or spreadsheet-level access to the same model can opt for those managed interfaces.
The codebase supports optional fine-tuning via HuggingFace Transformers and PEFT LoRA adapters, letting practitioners adapt the pretrained base to domain-specific distributions without retraining from scratch. A growing set of worked examples covers anomaly detection via quantile intervals, covariate-driven forecasting, and global temperature prediction.
