Inconsistent Multivariate Time Series Forecasting

Traditional statistical time series forecasting models rely on model identification methods to identify the worthiest model variants to investigate; therefore, the model parameters change with the statistical features of rolling windows to reach optimality. Currently, although deep-learning-based methods achieve promising multivariate forecasting performance, their representations of variable correlations are consistent regardless of the observed local time series properties and dynamic cross-variable relations, rendering them prone to overfitting. To bridge this gap, we propose FPPformer-MD, a novel inconsistent time series forecasting transformer. FPPformer-MD leverages multiresolution analysis to transform each univariate series into multiple frequency scales and evaluate the local variable correlations via their variances. Thus, FPPformer-MD receives richer input features, and its inner inconsistent cross-variable attention mechanism enables the adaptive extraction of cross-variable features. To further alleviate the overfitting problem, we apply dynamic mode decomposition to perform cross-variable data augmentation, which reconstructs the sequence outliers with other correlated sequences during the model training process. Extensive experiments conducted on thirteen real-world benchmarks demonstrate the state-of-the-art performance of FPPformer-MD.