STTS-EAD: Improving Spatio-Temporal Learning Based Time Series Prediction via

• URL: http://arxiv.org/abs/2501.07814v1
• Date: Tue, 14 Jan 2025 03:26:05 GMT
• Title: STTS-EAD: Improving Spatio-Temporal Learning Based Time Series Prediction via
• Authors: Yuanyuan Liang, Tianhao Zhang, Tingyu Xie,
• Abstract summary: We propose STTS-EAD, an end-to-end method that seamlessly integrates anomaly into the training process of time series forecasting. Our proposed STTS-EAD leveragestemporal information for forecasting and anomaly detection, with the two parts alternately executed and optimized for each other. Our experiments show that our proposed method can effectively process anomalies detected in the training stage to improve forecasting performance in the inference stage and significantly outperform baselines.
• Score: 7.247017092359663
• License:
• Abstract: Handling anomalies is a critical preprocessing step in multivariate time series prediction. However, existing approaches that separate anomaly preprocessing from model training for multivariate time series prediction encounter significant limitations. Specifically, these methods fail to utilize auxiliary information crucial for identifying latent anomalies associated with spatiotemporal factors during the preprocessing stage. Instead, they rely solely on data distribution for anomaly detection, which can result in the incorrect processing of numerous samples that could otherwise contribute positively to model training. To address this, we propose STTS-EAD, an end-to-end method that seamlessly integrates anomaly detection into the training process of multivariate time series forecasting and aims to improve Spatio-Temporal learning based Time Series prediction via Embedded Anomaly Detection. Our proposed STTS-EAD leverages spatio-temporal information for forecasting and anomaly detection, with the two parts alternately executed and optimized for each other. To the best of our knowledge, STTS-EAD is the first to integrate anomaly detection and forecasting tasks in the training phase for improving the accuracy of multivariate time series forecasting. Extensive experiments on a public stock dataset and two real-world sales datasets from a renowned coffee chain enterprise show that our proposed method can effectively process detected anomalies in the training stage to improve forecasting performance in the inference stage and significantly outperform baselines.

Related papers

• MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series [54.91026286579748]
  We propose a Multi-Grained Correlations-based Prediction Network. It simultaneously considers correlations at three levels to enhance prediction performance. It employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level.
  arXiv  Detail & Related papers  (2024-05-30T03:32:44Z)
• Loss Shaping Constraints for Long-Term Time Series Forecasting [79.3533114027664]
  We present a Constrained Learning approach for long-term time series forecasting that respects a user-defined upper bound on the loss at each time-step. We propose a practical Primal-Dual algorithm to tackle it, and aims to demonstrate that it exhibits competitive average performance in time series benchmarks, while shaping the errors across the predicted window.
  arXiv  Detail & Related papers  (2024-02-14T18:20:44Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Successive Model-Agnostic Meta-Learning for Few-Shot Fault Time Series Prognosis [3.5573601621032944]
  We introduce a novel 'pseudo meta-task' partitioning scheme that treats a continuous time period of a time series as a meta-task. Employing continuous time series as pseudo meta-tasks allows our method to extract more comprehensive features and relationships from the data. We introduce a differential algorithm to enhance the robustness of our method across different datasets.
  arXiv  Detail & Related papers  (2023-11-04T02:07:47Z)
• Performative Time-Series Forecasting [71.18553214204978]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective. We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts. We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-09T18:34:29Z)
• CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection [53.83593870825628]
  One main challenge in time series anomaly detection (TSAD) is the lack of labelled data in many real-life scenarios. Most of the existing anomaly detection methods focus on learning the normal behaviour of unlabelled time series in an unsupervised manner. We introduce a novel end-to-end self-supervised ContrAstive Representation Learning approach for time series anomaly detection.
  arXiv  Detail & Related papers  (2023-08-18T04:45:56Z)
• ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection [44.21198064126152]
  We propose a novel anomaly detection framework named ImDiffusion. ImDiffusion combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets.
  arXiv  Detail & Related papers  (2023-07-03T04:57:40Z)
• Better Batch for Deep Probabilistic Time Series Forecasting [15.31488551912888]
  We propose an innovative training method that incorporates error autocorrelation to enhance probabilistic forecasting accuracy. Our method constructs a mini-batch as a collection of $D$ consecutive time series segments for model training. It explicitly learns a time-varying covariance matrix over each mini-batch, encoding error correlation among adjacent time steps.
  arXiv  Detail & Related papers  (2023-05-26T15:36:59Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• Monte Carlo EM for Deep Time Series Anomaly Detection [6.312089019297173]
  Time series data are often corrupted by outliers or other kinds of anomalies. Recent approaches to anomaly detection and forecasting assume that the proportion of anomalies in the training data is small enough to ignore. We present a technique for augmenting existing time series models so that they explicitly account for anomalies in the training data.
  arXiv  Detail & Related papers  (2021-12-29T07:52:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.