Applied integration of time series and multi-variable regression algorithms

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Koyuncu F., Yücel A.

İstatistikçiler Dergisi:İstatistik ve Aktüerya, vol.14, no.1, pp.13-25, 2021 (Peer-Reviewed Journal)

  • Publication Type: Article / Article
  • Volume: 14 Issue: 1
  • Publication Date: 2021
  • Journal Name: İstatistikçiler Dergisi:İstatistik ve Aktüerya
  • Journal Indexes: TR DİZİN (ULAKBİM)
  • Page Numbers: pp.13-25
  • Ankara Yıldırım Beyazıt University Affiliated: Yes


Time Series (TS) based prediction models generate prediction based data that is supposed to be similar to the future data at a certain level. In this study, we designed new modeling that increases the prediction performance of the TS algorithm. The main purpose of the new modeling is to integrate the Multivariate-Adaptive-Regression-Splines (MARSplines) algorithm into the TS algorithm. Five-year Tokyo Stock Exchange data is analyzed as a case study to apply the relevant models. The results show that the new regression-based approach significantly improves the prediction performance of the time series algorithm.