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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.04377 |
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| _version_ | 1866909359520350208 |
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| author | You, Jiang Cela, Arben Natowicz, René Ouanounou, Jacob Siarry, Patrick |
| author_facet | You, Jiang Cela, Arben Natowicz, René Ouanounou, Jacob Siarry, Patrick |
| contents | Anomaly detection in time series data is a critical challenge across various domains. Traditional methods typically focus on identifying anomalies in immediate subsequent steps, often underestimating the significance of temporal dynamics such as delay time and horizons of anomalies, which generally require extensive post-analysis. This paper introduces a novel approach for time series anomaly prediction, incorporating temporal information directly into the prediction results. We propose a new dataset specifically designed to evaluate this approach and conduct comprehensive experiments using several state-of-the-art methods. Our results demonstrate the efficacy of our approach in providing timely and accurate anomaly predictions, setting a new benchmark for future research in this field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04377 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Anomaly Prediction: A Novel Approach with Explicit Delay and Horizon You, Jiang Cela, Arben Natowicz, René Ouanounou, Jacob Siarry, Patrick Machine Learning Artificial Intelligence Anomaly detection in time series data is a critical challenge across various domains. Traditional methods typically focus on identifying anomalies in immediate subsequent steps, often underestimating the significance of temporal dynamics such as delay time and horizons of anomalies, which generally require extensive post-analysis. This paper introduces a novel approach for time series anomaly prediction, incorporating temporal information directly into the prediction results. We propose a new dataset specifically designed to evaluate this approach and conduct comprehensive experiments using several state-of-the-art methods. Our results demonstrate the efficacy of our approach in providing timely and accurate anomaly predictions, setting a new benchmark for future research in this field. |
| title | Anomaly Prediction: A Novel Approach with Explicit Delay and Horizon |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2408.04377 |