Empirical Mode Decomposition Based Data Augmentation for Time Series Prediction Using NARX Network

Neural networks (NNs) have recently achieved significant performance gains for time series forecasting. However, they require large amounts of data to train. Data augmentation techniques have been suggested to improve the network training performance. Here, we adopt Empirical Mode Decomposition (EMD) as a data augmentation technique. The intrinsic mode functions (IMFs) produced by EMD are recombined with different weights to obtain surrogate data series, which are used to train a neural network for forecasting. We use M4 time series dataset and a custom nonlinear auto-regressive network with exogenous inputs (NARX) for the validation of the proposed method. The experimental results show an improvement in forecasting accuracy over a baseline method when using EMD based data augmentation.