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A Decoder-only Foundation Model For Time-series Forecasting

Abhimanyu Das, Weihao Kong, Rajat Sen, Yichen Zhou . Arxiv 2023 – 41 citations

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Compositional Generalization Datasets Interdisciplinary Approaches Multimodal Semantic Representation Time Series

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.

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