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

DOI

10.2991/amms-17.2017.29How to use a DOI?

Keywords

time series; automatic optimization search method; the fractional sum function of ASOD; the inverse function of fractional sum function of ASOD; the forecasting function of ASOD

Abstract

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.

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  - A Set of Time Series Forecasting Models Based on the Ordered Difference
BT  - Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)
PB  - Atlantis Press
SP  - 128
EP  - 131
SN  - 1951-6851
UR  - https://doi.org/10.2991/amms-17.2017.29
DO  - 10.2991/amms-17.2017.29
ID  - Wang2017/11
ER  -