SETTer 模型提升长期多变量时间序列预测准确率
SETTer: Sparse-Encoder Transformer for Long-term Multivariate Time Series Forecasting
这篇论文介绍了一个叫 SETTer 的新模型，专门用来预测长期的多变量时间序列数据，比如电力系统或交易数据，效果比很多现有模型好。
这篇论文提出了一种名为 SETTer 的稀疏编码Transformer模型，用于解决长期多变量时间序列预测问题。该模型通过解耦自注意力机制和混合掩码技术，有效捕捉了时间序列中的主导短期和长期模式。在多个真实世界基准数据集上，SETTer 在88%的测试场景中超越了现有最先进模型。
SETTer: Sparse-Encoder Transformer for Long-term Multivariate Time Series Forecasting
Long-term multivariate time series plays a significant role in many application areas such as power systems, trading, etc. However, their accurate prediction is quite difficult for conventional forecasting methods as they often exhibit high dimensionality and complex relationships. Recent works show that transformer-based approaches are quite effective for long-term forecasting thanks to their attention mechanism. However, in the presence of complex high-dimensional inputs, they show evidence of oversmoothing, limited capacity, and opacity. To this end, this paper introduces SETTer, a transformer-based model that addresses these challenges by incorporating novel techniques for decoupled self-attention and hybrid masking. The proposed techniques enable SETTer to effectively capture the dominant short- and long-term patterns across the temporal and channel dimensions. In addition, we enrich the model layers with simple explainable structures that indicate the discriminative pattern of SETTer. We show that with a single-layer transformer architecture, SETTer can effectively model long-term dependencies in the presence of varying data complexities. Extensive experiments on real-word benchmark datasets for long-term multivariate time series forecasting demonstrate that SETTer outperforms state-of-the-art models in 88% of the scenarios.