Large Pre-trained time series models for cross-domain Time series analysis tasks

• URL: http://arxiv.org/abs/2311.11413v2
• Date: Thu, 11 Jul 2024 16:32:12 GMT
• Title: Large Pre-trained time series models for cross-domain Time series analysis tasks
• Authors: Harshavardhan Kamarthi, B. Aditya Prakash,
• Abstract summary: We propose a novel method of textitadaptive 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.
• Score: 20.228846068418765
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 \textit{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.

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