A time series forecasting model based on linguistic forecasting rules

Abstract

The fuzzy time series (FTS) forecasting models have been studied intensively over the past few years. The existing FTS forecasting models partition the historical data into subintervals and assign the fuzzy sets to them by the human expert’s experience. Hieu et al. proposed a linguistic time series by utilizing the hedge algebras quantification to converse the numerical time-series data to the linguistic time series. Similar to the FTS forecasting models, the obtained linguistic time series can define the linguistic, logical relationships which are used to establish the linguistic, logical relationship groups and form a linguistic forecasting model. In this paper, we propose a linguistic time series forecasting model based on the linguistic forecasting rules induced from the linguistic, logical relationships instead of the linguistic, logical relationship groups proposed by Hieu. The experimental studies using the historical data of the enrollments of University of Alabama and the daily average temperature data in Taipei show the outperformance of the proposed forecasting models over the counterpart ones. Then, to realize the proposed models in Viet Nam, they are also applied to the forecasting problem of the historical data of the average rice production of Viet Nam from 1990 to 2010.