A Variable Adaptive Embedding Transformer for Multivariate Time Series Forecasting

Multivariate Time Series (MTS) forecasting is crucial in many domains, such as financial market analysis, weather forecasting and energy management. Among various solutions for this task, transformer-based models are popular due to their excellent ability capturing long-term dependencies. However, those models still face challenges for precise MTS forecasting due to the ignorance of variables' correlations and the complexity of the attention calculation. To address these challenges, we propose a novel Variable Adaptive Embedding transformer, namely VAEformer. Specifically, on the one hand, we employ variable adaptive embedding to capture correlations among variables better. On the other hand, we employ low-complexity linear layers to model temporal dependencies to improve the efficiency. Furthermore, the excellent predictive performance on real-world datasets from various domains suggests that our VAEformer can serve as a cross-domain tool, fostering collaboration and coordination among researchers from different fields.