TEAFormers: TEnsor-Augmented Transformers for Multi-Dimensional Time Series Forecasting
This addresses the challenge of effectively modeling multi-dimensional time series data in fields like economics and climate science, though it appears incremental as it builds on existing Transformer architectures.
The paper tackled the problem of multi-dimensional time series forecasting by introducing the Tensor-Augmented Transformer (TEAFormer), which preserves multi-dimensional structures to improve prediction accuracy and reduce computational costs, with experiments showing significant performance enhancements on real-world benchmarks.
Multi-dimensional time series data, such as matrix and tensor-variate time series, are increasingly prevalent in fields such as economics, finance, and climate science. Traditional Transformer models, though adept with sequential data, do not effectively preserve these multi-dimensional structures, as their internal operations in effect flatten multi-dimensional observations into vectors, thereby losing critical multi-dimensional relationships and patterns. To address this, we introduce the Tensor-Augmented Transformer (TEAFormer), a novel method that incorporates tensor expansion and compression within the Transformer framework to maintain and leverage the inherent multi-dimensional structures, thus reducing computational costs and improving prediction accuracy. The core feature of the TEAFormer, the Tensor-Augmentation (TEA) module, utilizes tensor expansion to enhance multi-view feature learning and tensor compression for efficient information aggregation and reduced computational load. The TEA module is not just a specific model architecture but a versatile component that is highly compatible with the attention mechanism and the encoder-decoder structure of Transformers, making it adaptable to existing Transformer architectures. Our comprehensive experiments, which integrate the TEA module into three popular time series Transformer models across three real-world benchmarks, show significant performance enhancements, highlighting the potential of TEAFormers for cutting-edge time series forecasting.