NFIG-X: Nonlinear Fuzzy Information Granule Series for Long-Term Traffic Flow Time-Series Forecasting

Yue Cheng, Weiwei Xing, Witold Pedrycz, Sidong Xian, Weibin Liu

Abstract: Long-term time-series forecasting is an extensive research topic and is of great significance in many fields. However, the task of long-term time-series forecasting is accompanied by the problem of increasing cumulative error and decreasing time correlation. To overcome these shortcomings, this article proposes a prediction framework based on the nonlinear fuzzy information granule (NFIG) series, which can boost the long-term performance of most predictors. First, we propose the representation of the NFIG for the first time, replacing the linear core lines with nonlinear time-dependent curves. Second, we propose a temporal window splitting algorithm based on curvature equations and weighted directed graphs, which can not only merge temporal windows with the same trend but also cointegrate incremental data. Finally, the nonlinear trend fuzzy granulation can be employed as a data preprocessing module for various time-series predictors to achieve a better long-term forecasting performance. As a typical time-series forecasting task, the precise long-term forecast of traffic flow data can relieve the overburdened traffic system and improve the traffic environment to a certain extent. Thus, the proposed method is employed for the long-term traffic flow forecasting. Compared with existing forecasting models, which achieves superior performances.

External IDs:doi:10.1109/tfuzz.2023.3261893