A Set of Ternary Time Series Forecasting Models Based on the Difference Rate
Xiaoli Lu, Hongxu Wang, Chengguo Yin, Hao Feng, Qingyan Wu · DEStech Transactions on Computer Science and Engineering · 2018
A set of ternary time series forecasting models based on the difference rate (ASTDR) is proposed. For an arbitrary time series, we can apply automatic optimization search method to sieve the ordinary time series forecasting model in ASTDR. For example, when simulating the prediction of the enrollments of University of Alabama in 1971–1992, we can apply automatic optimization search method to sieve the ordinary time series forecasting model Ft(0.000003,0.7, 0.000003) in ASTDR. The mean square error (MSE) and the average forecasting error rate (AFER) of the predicted values of the enrollments can reach MSE=0 and AFER=0%. The prediction accuracy of simulating the prediction of historical data of time series reaches the most ideal level.