TimeFound: A Foundation Model for Time Series Forecasting
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866915184076914688 |
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| author | Xiao, Congxi Zhou, Jingbo Xiao, Yixiong Lu, Xinjiang Zhang, Le Xiong, Hui |
| author_facet | Xiao, Congxi Zhou, Jingbo Xiao, Yixiong Lu, Xinjiang Zhang, Le Xiong, Hui |
| contents | We present TimeFound, an encoder-decoder transformer-based time series foundation model for out-of-the-box zero-shot forecasting. To handle time series data from various domains, TimeFound employs a multi-resolution patching strategy to capture complex temporal patterns at multiple scales. We pre-train our model with two sizes (200M and 710M parameters) on a large time-series corpus comprising both real-world and synthetic datasets. Over a collection of unseen datasets across diverse domains and forecasting horizons, our empirical evaluations suggest that TimeFound can achieve superior or competitive zero-shot forecasting performance, compared to state-of-the-art time series foundation models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04118 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TimeFound: A Foundation Model for Time Series Forecasting Xiao, Congxi Zhou, Jingbo Xiao, Yixiong Lu, Xinjiang Zhang, Le Xiong, Hui Machine Learning We present TimeFound, an encoder-decoder transformer-based time series foundation model for out-of-the-box zero-shot forecasting. To handle time series data from various domains, TimeFound employs a multi-resolution patching strategy to capture complex temporal patterns at multiple scales. We pre-train our model with two sizes (200M and 710M parameters) on a large time-series corpus comprising both real-world and synthetic datasets. Over a collection of unseen datasets across diverse domains and forecasting horizons, our empirical evaluations suggest that TimeFound can achieve superior or competitive zero-shot forecasting performance, compared to state-of-the-art time series foundation models. |
| title | TimeFound: A Foundation Model for Time Series Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.04118 |