A short-term forecasting for multi-factor time series with multiple linear trend fuzzy information granule and cross-association

Abstract

Multi-factor time series forecasting is of great significance in research and application, where capturing data characteristic and association are the main works. For data characteristic, the multiple linear trend fuzzy information granule is developed on multi-factor time series. This kind of granule accurately describes the multi-linear-trend information within the data, and exhibits high semantic and temporal interpretation. To distinguish the diverse trend information hidden in such granule, a fuzzy information granule clustering algorithm is raised, yielding the multi-factor cluster label series. Notably, each cluster label represents a class of trend patterns. Leveraging the characterized trend information, two multi-factor fuzzy association rules are mined, the multi-factor cluster label association rule and the multi-factor cluster label cross-association rule, reflecting the association and cross-association in multi-factor time series respectively. By combing the excavated data characteristic with fuzzy association rules, a short-term forecasting model is designed. This model wins the smallest root mean squared error, mean absolute percentage error, and mean absolute percentage error values in five stock time series forecasting analysis after comparing with other models, and the prediction comparisons of a statistical index (Wilcoxon signed rank test) are smaller than 0.05. The superiority of the novel forecasting model can be demonstrated through the performance across various metrics and indicators.