Transformer Multivariate Forecasting: Less is More?
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866917607196590080 |
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| author | Xu, Jingjing Wu, Caesar Li, Yuan-Fang Bouvry, Pascal |
| author_facet | Xu, Jingjing Wu, Caesar Li, Yuan-Fang Bouvry, Pascal |
| contents | In the domain of multivariate forecasting, transformer models stand out as powerful apparatus, displaying exceptional capabilities in handling messy datasets from real-world contexts. However, the inherent complexity of these datasets, characterized by numerous variables and lengthy temporal sequences, poses challenges, including increased noise and extended model runtime. This paper focuses on reducing redundant information to elevate forecasting accuracy while optimizing runtime efficiency. We propose a novel transformer forecasting framework enhanced by Principal Component Analysis (PCA) to tackle this challenge. The framework is evaluated by five state-of-the-art (SOTA) models and four diverse real-world datasets. Our experimental results demonstrate the framework's ability to minimize prediction errors across all models and datasets while significantly reducing runtime. From the model perspective, one of the PCA-enhanced models: PCA+Crossformer, reduces mean square errors (MSE) by 33.3% and decreases runtime by 49.2% on average. From the dataset perspective, the framework delivers 14.3% MSE and 76.6% runtime reduction on Electricity datasets, as well as 4.8% MSE and 86.9% runtime reduction on Traffic datasets. This study aims to advance various SOTA models and enhance transformer-based time series forecasting for intricate data. Code is available at: https://github.com/jingjing-unilu/PCA_Transformer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00230 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Transformer Multivariate Forecasting: Less is More? Xu, Jingjing Wu, Caesar Li, Yuan-Fang Bouvry, Pascal Machine Learning Artificial Intelligence In the domain of multivariate forecasting, transformer models stand out as powerful apparatus, displaying exceptional capabilities in handling messy datasets from real-world contexts. However, the inherent complexity of these datasets, characterized by numerous variables and lengthy temporal sequences, poses challenges, including increased noise and extended model runtime. This paper focuses on reducing redundant information to elevate forecasting accuracy while optimizing runtime efficiency. We propose a novel transformer forecasting framework enhanced by Principal Component Analysis (PCA) to tackle this challenge. The framework is evaluated by five state-of-the-art (SOTA) models and four diverse real-world datasets. Our experimental results demonstrate the framework's ability to minimize prediction errors across all models and datasets while significantly reducing runtime. From the model perspective, one of the PCA-enhanced models: PCA+Crossformer, reduces mean square errors (MSE) by 33.3% and decreases runtime by 49.2% on average. From the dataset perspective, the framework delivers 14.3% MSE and 76.6% runtime reduction on Electricity datasets, as well as 4.8% MSE and 86.9% runtime reduction on Traffic datasets. This study aims to advance various SOTA models and enhance transformer-based time series forecasting for intricate data. Code is available at: https://github.com/jingjing-unilu/PCA_Transformer. |
| title | Transformer Multivariate Forecasting: Less is More? |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.00230 |