{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"TimesFM is a pretrained time-series foundation model from Google Research that generates point and quantile forecasts for time-series data using a decoder-only transformer architecture.","skillfed_tags":["time-series-forecasting","foundation-model","transformer"],"use_cases":["Generate multi-step-ahead forecasts with confidence intervals for financial or operational time series","Fine-tune the pretrained model on domain-specific data using LoRA and HuggingFace Transformers","Integrate time-series predictions into BigQuery ML or Google Sheets workflows via the open-source API","Build agentic systems that call TimesFM as a forecasting skill for automated decision-making","Benchmark decoder-only architectures against traditional or RNN-based forecasting methods"],"what_it_does":"TimesFM is a decoder-only foundation model pretrained by Google Research specifically for time-series forecasting. It accepts historical time-series data and produces both point forecasts and quantile forecasts (percentile ranges) for future time steps. The model is available in different parameter sizes and supports context lengths up to 16k tokens, making it suitable for both short and long historical sequences.\n\nThe package provides a straightforward API: load a pretrained checkpoint from Hugging Face, configure forecast parameters (horizon, context length, quantile settings), and call forecast() with your time-series arrays. It supports optional backends (torch or Flax) for inference acceleration and optional covariate inputs via XReg. The model handles normalization internally and includes flags for specialized behaviors like flip invariance and quantile crossing fixes.","worth_installing":"Yes. TimesFM is actively maintained, has no known vulnerabilities, installs with low friction, and is backed by published research and Google's production deployments. Install it if you need a modern pretrained foundation model for time-series forecasting; the permissive Apache-2.0 license and optional backends (torch/Flax) make it flexible for both research and production use."},"id":"timesfm","links":{"html":"https://skillfed.io/packages/timesfm","md":"https://skillfed.io/packages/timesfm.md","pypi":"https://pypi.org/project/timesfm/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-02","license_spdx":null,"license_treatment":"permissive","name":"timesfm","python_support":"supports_current","summary":"A time series foundation model."},"popularity":{"monthly_downloads":284286,"position":8064,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.0.2"}