Transformer Architectures for Multivariate Time Series Forecasting

Abstract

Multivariate time series data constitutes the fundamental basis for decision making across real-world scenarios such as financial trading, intensive care, energy dispatch and the Internet of Things. Traditional statistical methods and early deep learning architectures including recurrent neural networks and convolutional neural networks expose structural bottlenecks when these models capture long-range dynamic dependencies, process high-dimensional cross correlations and accommodate data expansion on a massive scale. The Transformer architecture relies on the self-attention mechanism to break the traditional paradigm of sequence modeling through parallel computing capabilities and flexible global receptive fields. This architecture has emerged as the cutting-edge foundation for the analysis of multivariate time series. However, the standard Transformer model remains restricted by quadratic computational complexity, data starvation effects and the lack of interpretability associated with black box characteristics when this model processes real-world data featuring high-frequency fluctuations, non-stationarity and significant noise interference. This paper systematically synthesizes the key theoretical and methodological breakthroughs in the field of time series forecasting in recent years. This work starts from the underlying feature representation mechanism to deeply deconstruct data representation strategies which include dimensionality reduction decomposition, time-frequency domain conversion and masked pre-training. By integrating specific application scenarios, this paper conducts a fine-grained dynamic classification and performance evaluation of current deep learning models. This evaluation specifically focuses on efficient sparse attention, sequence decomposition, patch-based designs and multimodal hybrid Transformer architectures. Building upon this foundation, this work examines the severe challenges facing the field and prospectively explores future academic trajectories which encompass time series foundation models, physics-informed design, counterfactual reasoning and federated learning. The ultimate goal of this paper is to provide a panoramic reference for the theoretical breakthroughs and engineering deployment of complex time series analysis.