Particle swarm optimization-based empirical mode decomposition predictive technique for nonstationary data

Abstract

Real-world nonstationary data are usually characterized by high nonlinearity and complex patterns due to the effects of different exogenous factors that make prediction a very challenging task. An ensemble strategically combines multiple techniques and tends to be robust and more precise compared to a single intelligent algorithmic model. In this work, a dynamic particle swarm optimization-based empirical mode decomposition ensemble is proposed for nonstationary data prediction. The proposed ensemble implements an environmental change detection technique to capture concept drift occurring and the intrinsic nonlinearity in time series, hence improving prediction accuracy. The proposed ensemble technique was experimentally evaluated on electric time series datasets. The obtained results show that the proposed technique improves prediction accuracy and it outperformed several state-of-the-art techniques in several cases. For future work direction, a detailed empirical analysis of the proposed technique can be considered such as the effect of the cost of prediction errors, and the technique's search capability.