A decoder-only foundation model for time-series forecasting

Code

• github.com/google-research/timesfm
  Officialjax★ 10,100
• github.com/etna-team/etna
  pytorch★ 193

Abstract

Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset. Our model is based on pretraining a patched-decoder style attention model on a large time-series corpus, and can work well across different forecasting history lengths, prediction lengths and temporal granularities.

Tasks

Benchmark Results

Reproductions