A0858
Title: TEAFormers: Tensor-augmented transformers for multi-dimensional time series forecasting
Authors: Yuzhou Chen - University of California, Riverside (United States) [presenting]
Abstract: 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. The tensor-augmented transformer (TEAFormer) is introduced, 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 comprehensive experiments, which integrate the TEA module into three popular time series transformer models across real-world digital finance data, show significant performance enhancements, highlighting the potential of TEAFormers for cutting-edge time series forecasting.
