DeformTime: Capturing Variable Dependencies with Deformable Attention for Time Series Forecasting
This addresses the limitation of overlooking exogenous variables in forecasting for domains like infectious disease modeling, though it appears incremental as it builds on existing attention-based methods.
The paper tackled the problem of multivariable time series forecasting by proposing DeformTime, a neural network architecture that captures correlated temporal patterns from input space to improve accuracy, resulting in a 7.2% average reduction in mean absolute error across most tasks.
In multivariable time series (MTS) forecasting, existing state-of-the-art deep learning approaches tend to focus on autoregressive formulations and often overlook the potential of using exogenous variables in enhancing the prediction of the target endogenous variable. To address this limitation, we present DeformTime, a neural network architecture that attempts to capture correlated temporal patterns from the input space, and hence, improve forecasting accuracy. It deploys two core operations performed by deformable attention blocks (DABs): learning dependencies across variables from different time steps (variable DAB), and preserving temporal dependencies in data from previous time steps (temporal DAB). Input data transformation is explicitly designed to enhance learning from the deformed series of information while passing through a DAB. We conduct extensive experiments on 6 MTS data sets, using previously established benchmarks as well as challenging infectious disease modelling tasks with more exogenous variables. The results demonstrate that DeformTime improves accuracy against previous competitive methods across the vast majority of MTS forecasting tasks, reducing the mean absolute error by 7.2% on average. Notably, performance gains remain consistent across longer forecasting horizons.