Scalable Transformer for High Dimensional Multivariate Time Series Forecasting

• URL: http://arxiv.org/abs/2408.04245v1
• Date: Thu, 8 Aug 2024 06:17:13 GMT
• Title: Scalable Transformer for High Dimensional Multivariate Time Series Forecasting
• Authors: Xin Zhou, Weiqing Wang, Wray Buntine, Shilin Qu, Abishek Sriramulu, Weicong Tan, Christoph Bergmeir,
• Abstract summary: This study investigates the reasons behind the suboptimal performance of channel-dependent models on high-dimensional MTS data. We propose STHD, the Scalable Transformer for High-Dimensional Multidimensional Time Series Forecasting. Experiments show STHD's considerable improvement on three high-dimensional datasets: Crime-Chicago, Wiki-People, and Traffic.
• Score: 10.17270031004674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep models for Multivariate Time Series (MTS) forecasting have recently demonstrated significant success. Channel-dependent models capture complex dependencies that channel-independent models cannot capture. However, the number of channels in real-world applications outpaces the capabilities of existing channel-dependent models, and contrary to common expectations, some models underperform the channel-independent models in handling high-dimensional data, which raises questions about the performance of channel-dependent models. To address this, our study first investigates the reasons behind the suboptimal performance of these channel-dependent models on high-dimensional MTS data. Our analysis reveals that two primary issues lie in the introduced noise from unrelated series that increases the difficulty of capturing the crucial inter-channel dependencies, and challenges in training strategies due to high-dimensional data. To address these issues, we propose STHD, the Scalable Transformer for High-Dimensional Multivariate Time Series Forecasting. STHD has three components: a) Relation Matrix Sparsity that limits the noise introduced and alleviates the memory issue; b) ReIndex applied as a training strategy to enable a more flexible batch size setting and increase the diversity of training data; and c) Transformer that handles 2-D inputs and captures channel dependencies. These components jointly enable STHD to manage the high-dimensional MTS while maintaining computational feasibility. Furthermore, experimental results show STHD's considerable improvement on three high-dimensional datasets: Crime-Chicago, Wiki-People, and Traffic. The source code and dataset are publicly available https://github.com/xinzzzhou/ScalableTransformer4HighDimensionMTSF.git.

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