Detecting the Future: All-at-Once Event Sequence Forecasting with Horizon Matching
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915654810992640 |
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| author | Karpukhin, Ivan Savchenko, Andrey |
| author_facet | Karpukhin, Ivan Savchenko, Andrey |
| contents | Long-horizon events forecasting is a crucial task across various domains, including retail, finance, healthcare, and social networks. Traditional models for event sequences often extend to forecasting on a horizon using an autoregressive (recursive) multi-step strategy, which has limited effectiveness due to typical convergence to constant or repetitive outputs. To address this limitation, we introduce DEF, a novel approach for simultaneous forecasting of multiple future events on a horizon with high accuracy and diversity. Our method optimally aligns predictions with ground truth events during training by using a novel matching-based loss function. We establish a new state-of-the-art in long-horizon event prediction, achieving up to a 50% relative improvement over existing temporal point processes and event prediction models. Furthermore, we achieve state-of-the-art performance in next-event prediction tasks while demonstrating high computational efficiency during inference. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_13131 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detecting the Future: All-at-Once Event Sequence Forecasting with Horizon Matching Karpukhin, Ivan Savchenko, Andrey Machine Learning Artificial Intelligence Long-horizon events forecasting is a crucial task across various domains, including retail, finance, healthcare, and social networks. Traditional models for event sequences often extend to forecasting on a horizon using an autoregressive (recursive) multi-step strategy, which has limited effectiveness due to typical convergence to constant or repetitive outputs. To address this limitation, we introduce DEF, a novel approach for simultaneous forecasting of multiple future events on a horizon with high accuracy and diversity. Our method optimally aligns predictions with ground truth events during training by using a novel matching-based loss function. We establish a new state-of-the-art in long-horizon event prediction, achieving up to a 50% relative improvement over existing temporal point processes and event prediction models. Furthermore, we achieve state-of-the-art performance in next-event prediction tasks while demonstrating high computational efficiency during inference. |
| title | Detecting the Future: All-at-Once Event Sequence Forecasting with Horizon Matching |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2408.13131 |