Set of Time Series Forecasting Models Using the Ordered Difference

DOI

10.2991/amms-17.2017.28How to use a DOI?

Keywords

the fractional sum function Dw(x,y) of SOD; the inverse function Ew(x,y) of fractional sum function of SOD; the forecasting model Fw(x, y) of SOD; automatic optimization search method; time series

Abstract

Set of time series forecasting models using the ordered difference of historical data (SOD) is proposed. For a time series, for example, for the enrollment of the University of Alabama in 1971–1992, when simulating the prediction of historical data, the automatic optimization search method can be used to sieve the satisfactory time series forecasting model Fw(0.00003, 0.00003) in SOD, and the average forecasting error rate (AFER) of the predicted values can reach AFER=0% and the mean square error (MSE) is MSE=0. The problem that the prediction accuracy of the existing fuzzy time series forecasting models is not high has been solved.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hongxu Wang
AU  - Chengguo Yin
AU  - Xiaoli Lu
AU  - Hao Feng
AU  - Xiaofang Fu
PY  - 2017/11
DA  - 2017/11
TI  - Set of Time Series Forecasting Models Using the Ordered Difference
BT  - Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)
PB  - Atlantis Press
SP  - 124
EP  - 127
SN  - 1951-6851
UR  - https://doi.org/10.2991/amms-17.2017.28
DO  - 10.2991/amms-17.2017.28
ID  - Wang2017/11
ER  -