AE-DIL: A double incremental learning algorithm for non-stationary time series prediction via adaptive ensemble

Abstract

Many dynamic processes in the real world can be modeled as time series, so time series prediction is significant for social and economic development. The inherent non-stationarity of time series obtained from actual projects may make it difficult to predict accurately. To alleviate this problem, in this paper, a Double Incremental Learning algorithm via Adaptive Ensemble, termed as AE-DIL for short, is proposed for non-stationary time series prediction. AE-DIL provides a general online prediction framework consisting of two modules. The first detects changes based on the statistical hypothesis test and self-adaptive sliding window technology. The second updates the prediction model based on double incremental learning and adaptive ensemble learning. The effectiveness of the proposed algorithm is empirically underpinned by the experiments conducted on seven benchmark time series datasets, compared with several baselines and state-of-the-art models.