A hybrid method of time series forecasting based on information granulation and dynamic selection strategy¹

Article type: Research Article

Authors: Ma, Zhipeng^a | Guo, Hongyue^{b; *} | Wang, Lidong^a

Affiliations: [a] School of Science, Dalian Maritime University, Dalian, Liaoning, China | [b] School of Maritime Economics and Management, Dalian Maritime University, Dalian, Liaoning, China

Correspondence: [*] Corresponding author. Hongyue Guo, School of Maritime Economics and Management, Dalian Maritime University, Dalian, Liaoning, China 116026, Liaoning, China. Tel.: +86-411-84723837; E-mail: [email protected].

Note: [1] This work is supported in part by the Natural Science Foundation of China under Grant 62173053 and Grant 62006033, Dalian High-level Talents Innovation Support Plan 2021RQ061 and the Fundamental Research Funds for the Central Universities under Grant number 3132022639.

Abstract: Forecasting trend and variation ranges for time series has been challenging but crucial in real-world modeling. This study designs a hybrid time series forecasting (FIGDS) model based on granular computing and dynamic selection strategy. Firstly, with the guidance of the principle of justifiable granularity, a collection of interval-based information granules is formed to characterize variation ranges for time series on a specific time domain. After that, the original time series is transformed into granular time series, contributing to dealing with time series at a higher level of abstraction. Secondly, the L1 trend filtering method is applied to extract trend series and residual series. Furthermore, this study develops hybrid predictors of the trend series and residual series for forecasting the variation range of time series. The ARIMA model is utilized in the forecasting task of the residual series. The dynamic selection strategy is employed to identify the ideal forecasting models from the pre-trained multiple predictor system for forecasting the test pattern of the trend series. Eventually, the empirical experiments are carried out on ten time series datasets with a detailed comparison for validating the effectiveness and practicability of the established hybrid time series forecasting method.

Keywords: Granular computing, information granule, time series forecasting, dynamic selection, L₁ trend filtering

DOI: 10.3233/JIFS-222746

Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2367-2379, 2023