Affiliations: [a]
Department of Mathematics, College of Arts and Sciences, Shanghai Maritime University, Shanghai, China
| [b] Fujian Provincial Key Laboratory of Data Intensive Computing, Quanzhou Normal University, Quanzhou, China
| [c] Yangshan Port Maritime Safety Administration, Shanghai, Shanghai, China
Correspondence:
[*]
Corresponding author. Fang Li, Department of Mathematics, College of Arts and Sciences, Shanghai Maritime University, Shanghai 201306, China. Tel.: +86 21 38282259; E-mail: lifang5647@shmtu.edu.cn.
Abstract: In the existing short-term forecasting methods of time series, two challenges are faced: capture the associations of data and avoid cumulative errors. For tackling these challenges, the fuzzy information granule based model catches our attention. The rule used in this model is fuzzy association rule (FAR), in which the FAR is constructed from a premise granule to a consequent granule at consecutive time periods, and then it describes the short-association in data. However, in real time series, another association, the association between a premise granule and a consequent granule at non-consecutive time periods, frequently exists, especially in periodical and seasonal time series. While the existing FAR can’t express such association. To describe it, the fuzzy long-association rule (FLAR) is proposed in this study. This kind of rule reflects the influence of an antecedent trend on a consequent trend, where these trends are described by fuzzy information granules at non-consecutive time periods. Thus, the FLAR can describe the long-association in data. Correspondingly, the existing FAR is called as fuzzy short-association rule (FSAR). Combining the existing FSAR with FLAR, a novel short-term forecasting model is presented. This model makes forecasting at granular level, and then it reduces the cumulative errors in short-term prediction. Note that the prediction results of this model are calculated from the available FARs selected by the k-medoids clustering based rule selection algorithm, therefore they are logical and accurate. The better forecasting performance of this model has been verified by comparing it with existing models in experiments.
Keywords: Trend fuzzy information granule, fuzzy long-association rule, long-association, k-medoids clustering based rule selection algorithm, short-term forecasting
