chronos-t5-base

Model · Time series forecasting

chronos-t5-base

Autogluon

Update Feb 14, 2025: Chronos-Bolt & original Chronos models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. Update Nov 27, 2024: We have released Chronos-Bolt models that are more accurate (5% lower error), up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. Check out the new models here. Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these…

Model · Time series forecasting

chronos-bolt-base

Autogluon

Update Feb 14, 2025: Chronos-Bolt models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. Chronos-Bolt is a family of pretrained time series forecasting models which can be used for zero-shot forecasting. It is based on the T5 encoder-decoder architecture and has been trained on nearly 100 billion time series observations. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as…

Model · Time series forecasting

chronos-bolt-base

Amazon

Update Feb 14, 2025: Chronos-Bolt models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. Chronos-Bolt is a family of pretrained time series forecasting models which can be used for zero-shot forecasting. It is based on the T5 encoder-decoder architecture and has been trained on nearly 100 billion time series observations. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as…

Model · Time series forecasting

Aurora

DI DaSE ECNU

alt="Aurora Logo" src="https://cdn-uploads.huggingface.co/production/uploads/66276727368ec2a0b933772c/ytpsIAr98keUvNouoOVmb.png" width="30%" The official code repo of our ICLR 2026 paper: Aurora: Towards Universal Generative Multimodal Time Series Forecasting alt="ICLR 2026" src="https://img.shields.io/badge/ICLR%202026-Aurora-orange" alt="Python" src="https://img.shields.io/badge/Python-3.10%2B-blue" alt="PyTorch" src="https://img.shields.io/badge/PyTorch-2.4.1-blue" alt="GitHub Stars" src="https://img.shields.io/github/stars/decisionintelligence/Aurora?logo=github" alt="GitHub" src="https://img.shields.io/badge/GitHub-Aurora-black?logo=github" Aurora is a highly capable multimodal time…

Model · Time series forecasting

timesfm-2.5-200m-pytorch

Google

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. Please reinstall the latest version of the timesfm package to reflect these changes. Results should be unchanged. This checkpoint is not an officially supported Google product. See TimesFM in BigQuery for Google official support. timesfm-2.5-200m is the third open model checkpoint. timesfm-2.5-200m is pretrained using - Wikimedia Pageviews, cutoff Nov 2023 (see paper for details). - Google Trends top queries, cutoff EoY 2022 (see paper for details). - Synthetic and augmented data. At this point, please run

Model · Time series forecasting

timesfm-2.5-200m-transformers

Google

TimesFM (Time Series Foundation Model) is a pretrained decoder-only model for time-series forecasting. This repository contains the Transformers port of the official TimesFM 2.5 PyTorch release. This model is converted from the official TimesFM 2.5 PyTorch checkpoint and integrated into transformers as TimesFm25ModelForPrediction. The converted checkpoint preserves the original architecture and forecasting behavior, including: patch-based inputs for time-series contexts decoder-only self-attention stack point and quantile forecasts Weight conversion parity is verified by comparing converted-model forecasts against the official implementation outputs on deterministic inputs.