TripCast: Pre-training of Masked 2D Transformers for Trip Time Series Forecasting
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929557059141632 |
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| author | Liao, Yuhua Wang, Zetian Wei, Peng Nie, Qiangqiang Zhang, Zhenhua |
| author_facet | Liao, Yuhua Wang, Zetian Wei, Peng Nie, Qiangqiang Zhang, Zhenhua |
| contents | Deep learning and pre-trained models have shown great success in time series forecasting. However, in the tourism industry, time series data often exhibit a leading time property, presenting a 2D structure. This introduces unique challenges for forecasting in this sector. In this study, we propose a novel modelling paradigm, TripCast, which treats trip time series as 2D data and learns representations through masking and reconstruction processes. Pre-trained on large-scale real-world data, TripCast notably outperforms other state-of-the-art baselines in in-domain forecasting scenarios and demonstrates strong scalability and transferability in out-domain forecasting scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18612 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TripCast: Pre-training of Masked 2D Transformers for Trip Time Series Forecasting Liao, Yuhua Wang, Zetian Wei, Peng Nie, Qiangqiang Zhang, Zhenhua Machine Learning Artificial Intelligence Deep learning and pre-trained models have shown great success in time series forecasting. However, in the tourism industry, time series data often exhibit a leading time property, presenting a 2D structure. This introduces unique challenges for forecasting in this sector. In this study, we propose a novel modelling paradigm, TripCast, which treats trip time series as 2D data and learns representations through masking and reconstruction processes. Pre-trained on large-scale real-world data, TripCast notably outperforms other state-of-the-art baselines in in-domain forecasting scenarios and demonstrates strong scalability and transferability in out-domain forecasting scenarios. |
| title | TripCast: Pre-training of Masked 2D Transformers for Trip Time Series Forecasting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2410.18612 |