TimesFM is a high-performance, pretrained time-series forecasting model designed to predict future values based on historical data.
Source: per README View on GitHub →TimesFM is gaining attention due to its integration with Google's enterprise products like BigQuery ML and Google Sheets, addressing the need for scalable and reliable time-series forecasting in large-scale environments. Its unique architecture and support for various backends make it a versatile choice for developers.
Source: Synthesis of README and project traitsTimesFM comes with a pretrained model, allowing for efficient forecasting without the need for extensive training data or computational resources.
Source: per READMEThe project supports PyTorch, Flax, and XReg, enabling deployment on various hardware and infrastructure, including GPUs, TPUs, and CPUs.
Source: Dependency files + code treeTimesFM supports continuous quantile forecasting, providing a range of possible outcomes for future values, which is crucial for risk assessment.
Source: per READMEThe architecture is modular, with separate directories for different backends (PyTorch, Flax, XReg) and utilities. It employs design patterns such as dependency injection and separation of concerns, with a clear data flow from input preprocessing to forecasting and output generation.
Source: Code tree + dependency filesCenter: project; inner ring: core feature modules; outer ring: key dependencies. Auto-generated from core_features and tech_stack.key_deps.
numpyhuggingface_hubsafetensorstorchflaxoptaxeinshapeorbax-checkpointjaxtypingjaxscikit-learnTimesFM is suitable for enterprise-level time-series forecasting in applications such as financial market analysis, energy demand forecasting, and inventory management. It is also useful for personal projects requiring accurate time-series predictions.
Source: READMEv2.5 (2025-09-15): Reduced model size to 200M parameters, increased context length to 16k, added continuous quantile forecasting, and incorporated several improvements and fixes.
Source: GitHub ReleasesTimesFM is a powerful tool for time-series forecasting, particularly suitable for developers and enterprises requiring scalable and accurate predictions. Its modular architecture and support for various backends make it a versatile choice for different deployment scenarios.
TimesFM is a high-performance, pretrained time-series forecasting model designed to predict future values based on historical data.
timesfm's core features include: Pretrained Model, Support for Multiple Backends, Continuous Quantile Forecasting.
TimesFM is gaining attention due to its integration with Google's enterprise products like BigQuery ML and Google Sheets, addressing the need for scalable and reliable time-series forecasting in large-scale…
TimesFM is suitable for enterprise-level time-series forecasting in applications such as financial market analysis, energy demand forecasting, and inventory management.