LAUSR

Multivariate time series forecasting recently has received extensive attention with its wide application in finance, transportation, environment, and so on. However, few of the currently developed models have considered the impact of noise on prediction. Since multivariate time series contains multiple subsequences with strong nonlinear fluctuations, it is also difficult to obtain satisfactory prediction results. In this paper, aiming at improving prediction performance, we have proposed a novel ensemble three-phase model called adaptive noise reducer-stacked auto-encoder-validating-AdaBoost-based long short-term memory (ANR-SAE-VALSTM). We start with an introduction of a novel ANR for time series noise elimination. The SAEs are then used to extract features from the de-noised multivariate time series. Finally, we feed the de-noised features into the VALSTM to train an ensemble over-fitting prevention predictor. The proposed model is employed on the Beijing PM2.5 dataset and GEFCom2014 Electricity Price dataset. Compared with other popular models, the proposed model has achieved the best prediction performance in all prediction horizons. In addition, a careful ablation study is conducted to demonstrate the efficiency of our model design.