A decoder-only foundation model for time-series forecasting
Summary
This article presents a research paper on Time-Series Foundation Model (TimeFM), a decoder-only model that achieves near-optimal zero-shot performance across diverse time-series datasets by adapting large language model techniques.
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Paper page - A decoder-only foundation model for time-series forecasting
Source: https://huggingface.co/papers/2310.10688 Published on Oct 14, 2023
Abstract
A large language model adapted for time-series forecasting achieves near-optimal zero-shot performance on diverse datasets across different time scales and granularities.
Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model forforecastingwhose out-of-the-boxzero-shot performanceon a variety of public datasets comes close to the accuracy of state-of-the-art supervisedforecastingmodels for each individual dataset. Our model is based on pretraining apatched-decoderstyleattention modelon a largetime-series corpus, and can work well across differentforecastinghistory lengths, prediction lengths and temporal granularities.
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Models citing this paper14
#### google/timesfm-1.0-200m Time Series Forecasting• UpdatedMay 17, 2024 • 1.3k • 814
#### google/timesfm-2.0-500m-pytorch Time Series Forecasting• 0.5B• UpdatedApr 16, 2025 • 35.7k • 252
#### google/timesfm-2.5-200m-pytorch Time Series Forecasting• UpdatedOct 2, 2025 • 228
#### google/timesfm-2.5-200m-transformers Time Series Forecasting• 0.2B• Updated27 days ago • 145k • 82
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