An Improved Forecasting Model of Fuzzy Time Series

Abstract

Since Song and Chissom proposed fuzzy time series forecasting theory, already exceed in the 20 years. Scholars have proposed many fuzzy time series forecasting models, the prediction accuracy of historical simulation data continues to improve. Unfortunately has not hitherto given for fuzzy time series forecasting model about the data of unknown years. This paper presents an improved forecasting model of fuzzy time series. It may predict the historical simulation data, but also may predict the unknown year data.

Application of F5 (0.000001) in Forecasting Tourist Quantity of Sanya

“…These data are used to propose Definition 1 of fuzzy time series forecasting model with high accuracy. Wang et al [4][5][6][7][8] also developed several forecasting models with the basic variable of inverse fuzzy number. In the work, the forecasting models of Literature [4][5][6][7][8] were improved to simplify their expression forms of formulas.…”

A Set of Ternary Time Series Forecasting Models Based on the Difference Rate

“…The prediction accuracy of simulating the prediction of historical data of time series reaches the most ideal level. The forecasting model of this paper has obvious advantages over the existing fuzzy time series forecasting models (such as [5][6][7][8][9][10][11][12][13][14][15][16]). …”

Applying of F2 (0.004) Forecasting the Number of Tourists in Sanya

“…For example, Wang et al [10] proposed a method to obtain AFER = 0.1705% and MSE = 1121. In this article, the forecasting model in the literature [10] is modified, called F2 (0.004). It makes the prediction formula more streamlined.…”

“…In 2012, Saxena et al [5] proposed a new fuzzy time-series forecasting model based on inverse fuzzy numbers, making the average forecast error rate reach an unprecedented prediction accuracy: for the classic cases, the average forecast error rate: AFER = 0.3406% and the mean square error: MSE = 9169. Wang et al [6][7][8][9][10] made further improvements for the inverse fuzzy forecasting model, not only greatly simplifying the calculation processes, but improving the prediction accuracy when applied to the classic case. For example, Wang et al [10] proposed a method to obtain AFER = 0.1705% and MSE = 1121.…”

“…Wang et al [6][7][8][9][10] made further improvements for the inverse fuzzy forecasting model, not only greatly simplifying the calculation processes, but improving the prediction accuracy when applied to the classic case. For example, Wang et al [10] proposed a method to obtain AFER = 0.1705% and MSE = 1121. In this article, the forecasting model in the literature [10] is modified, called F2 (0.004).…”