A Set of Time Series Forecasting Models Based on the Ordered Difference

A set of time series forecasting models based on the ordered difference of historical data (ASOD) is proposed. For a time series, the automatic optimization search method can be applied to sieve standard time series forecasting model Cp(k, h) in ASOD, so that in simulating the prediction of historical data of the time series, the predicted values can reach AFER (Average Forecasting Error Rate) = 0% and MSE (Mean Square Error) = 0. For instance, for the enrollment of the University of Alabama in 1971- 1992, the automatic optimization search method can be applied to sieve standard time series forecasting model Cp(0.0003,0.0003), the problem that the prediction accuracy of fuzzy time series forecasting model is not ideal for many years has been solved.