TimeCNN通过独立卷积核提升多变量时序关系捕捉能力
TimeCNN: Refining Cross-Variable Interaction on Time Point for Time Series Forecasting
- 每个时间点使用独立卷积核，动态建模变量间相关性
- 在12个真实数据集上优于主流模型，计算量降低60.46%
- 适合需要高效高精度时序预测的工业场景
时序预测广泛应用于多个领域。基于Transformer的模型在建模跨时间与跨变量交互方面展现出巨大潜力，但现有方法难以捕捉多变量时序中复杂的（正负）相关性及其随时间的动态变化。为此，我们提出TimeCNN模型，通过引入时间点无关的独立卷积核机制，使每个时间点拥有独立的建模能力，以更精准捕捉变量间的复杂关系。该方法有效应对正负相关性和关系演化问题。在12个真实世界数据集上的大量实验表明，TimeCNN持续优于当前最优模型，计算开销减少约60.46%，参数量降低约57.50%，推理速度比基准iTransformer快3至4倍。
原文摘要 · Abstract (English)
Time series forecasting is extensively applied across diverse domains. Transformer-based models demonstrate significant potential in modeling cross-time and cross-variable interaction. However, we notice that the cross-variable correlation of multivariate time series demonstrates multifaceted (positive and negative correlations) and dynamic progression over time, which is not well captured by existing Transformer-based models. To address this issue, we propose a TimeCNN model to refine cross-variable interactions to enhance time series forecasting. Its key innovation is timepoint-independent, where each time point has an independent convolution kernel, allowing each time point to have its independent model to capture relationships among variables. This approach effectively handles both positive and negative correlations and adapts to the evolving nature of variable relationships over time. Extensive experiments conducted on 12 real-world datasets demonstrate that TimeCNN consistently outperforms state-of-the-art models. Notably, our model achieves significant reductions in computational requirements (approximately 60.46%) and parameter count (about 57.50%), while delivering inference speeds 3 to 4 times faster than the benchmark iTransformer model
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