The Set of Fuzzy Time Series Forecasting Models Based on the Ordered Difference Rate

Yin, Chengguo; Wang, Hongxu; Feng, Hao; Lü, Xiaoli

doi:10.2991/msam-17.2017.9

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2018

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“…Jilani, Burney and Ardil [2] put forward the new forecasting model in 2007, which had a good forecasting accuracy. Some scholars [3][4][5][6][7] improved forecasting model of Jilani, Burney and Ardil, and improved the forecasting accuracy. This paper prosed the set of ternary time series forecasting models based on the ordered difference of difference(STODD), which can also obtain satisfactory forecasting accuracy, that is mean square error MSE=0 and the average forecasting error rate of AFER=0% when it predicted the registered number at the University of Alabama in year 1971~1992 .…”

Section: Introductionmentioning

confidence: 99%

The Set of Ternary Time Series Forecasting Models Based on the Ordered Difference of Difference

Lü¹,

Wang²,

Yin³

et al. 2018

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Abstract. This paper proposed a set of ternary time series forecasting models based on the ordered difference of difference method. When predicting the registered number at the University of Alabama in 1971~1992 years, the computer can find forecasting model as Fx(0.00004,0.9,0.00004) using automatic optimization search method, which can make the mean square error MSE=0 and the average forecasting error rate AFER=0%. For any time series, this forecasting accuracy can be obtained, which improved comprehensively the forecasting accuracy of the existing fuzzy time series forecasting model.

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Section: Introductionmentioning

confidence: 99%