Interpretable predictive modeling of non-stationary long time series

Abstract

Highly accurate and interpretable forecasting of non-stationary long-time series is a challenge. It is often difficult for existing models to capture the trends of non-stationary time series and achieve interpretable forecasts. To this end, by integrating causal learning and high-order fuzzy cognitive graphs (HFCM), this paper proposes the Causal-HFCM framework. Firstly, the Peter-Clark (PC) algorithm is used to obtain the causal relationships of data, so that the relationships between data can be interpretable and causal learning breaks the limitation that the fuzzy cognitive graph only considers the correlation of data. Secondly, to capture the non-stationary property of time series and improve the prediction ability of fuzzy cognitive maps, a method combining the learning weight matrix algorithm of Huber regression with high-order fuzzy cognitive maps is proposed. Finally, the effectiveness of the proposed method is verified by conducting experiments with three different non-stationary time series datasets of traffic, air, and bearings.