Large Pre-trained time series models for cross-domain Time series analysis tasks
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866913598253563904 |
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| author | Kamarthi, Harshavardhan Prakash, B. Aditya |
| author_facet | Kamarthi, Harshavardhan Prakash, B. Aditya |
| contents | Large pre-trained models have been vital in recent advancements in domains like language and vision, making model training for individual downstream tasks more efficient and provide superior performance. However, tackling time-series analysis tasks usually involves designing and training a separate model from scratch leveraging training data and domain expertise specific to the task. We tackle a significant challenge for pre-training a foundational time-series model from multi-domain time-series datasets: extracting semantically useful tokenized inputs to the model across heterogenous time-series from different domains. We propose Large Pre-trained Time-series Models (LPTM) that introduces a novel method of adaptive segmentation that automatically identifies optimal dataset-specific segmentation strategy during pre-training. This enables LPTM to perform similar to or better than domain-specific state-of-art model when fine-tuned to different downstream time-series analysis tasks and under zero-shot settings. LPTM achieves superior forecasting and time-series classification results taking up to 40% less data and 50% less training time compared to state-of-art baselines. Code: www.github.com/AdityaLab/Samay |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_11413 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Large Pre-trained time series models for cross-domain Time series analysis tasks Kamarthi, Harshavardhan Prakash, B. Aditya Machine Learning Large pre-trained models have been vital in recent advancements in domains like language and vision, making model training for individual downstream tasks more efficient and provide superior performance. However, tackling time-series analysis tasks usually involves designing and training a separate model from scratch leveraging training data and domain expertise specific to the task. We tackle a significant challenge for pre-training a foundational time-series model from multi-domain time-series datasets: extracting semantically useful tokenized inputs to the model across heterogenous time-series from different domains. We propose Large Pre-trained Time-series Models (LPTM) that introduces a novel method of adaptive segmentation that automatically identifies optimal dataset-specific segmentation strategy during pre-training. This enables LPTM to perform similar to or better than domain-specific state-of-art model when fine-tuned to different downstream time-series analysis tasks and under zero-shot settings. LPTM achieves superior forecasting and time-series classification results taking up to 40% less data and 50% less training time compared to state-of-art baselines. Code: www.github.com/AdityaLab/Samay |
| title | Large Pre-trained time series models for cross-domain Time series analysis tasks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2311.11413 |