@inproceedings{86d5f767735c4723b5d336671528c267,
title = "Forecasting accuracy and change point detection",
abstract = "The accuracy of time series forecasting often decreases because of the existence of change points in the data. This paper presents a novel method for time series forecasting that taking into account the possibility of a change point in past data. The proposed method can be applied to situations where the considered time series consists of independent or weakly dependent observations. Change point analysis prevents the omission of relevant data as well as the forecasting that may be based on irrelevant data. The study demonstrates that change point techniques may increase the accuracy of forecasts.",
keywords = "Business forecasting, Change point, Error indexes, Homogeneous series",
author = "Gregory Gurevich and Yossi Hadad and Baruch Keren",
note = "Publisher Copyright: {\textcopyright} 2017 Slovenian Society Informatika. All Rights Reserved.; 14th International Symposium on Operational Research, SOR 2017 ; Conference date: 27-09-2017 Through 29-09-2017",
year = "2017",
month = jan,
day = "1",
language = "English",
series = "Proceedings of the 14th International Symposium on Operational Research, SOR 2017",
publisher = "SLOVENIAN SOCIETY INFORMATIKA",
pages = "314--319",
editor = "Samo Drobne and Janez Zerovnik and {Zadnik Stirn}, Lidija and {Kljajic Borstar}, Mirjana",
booktitle = "Proceedings of the 14th International Symposium on Operational Research, SOR 2017",
}