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Adding Additional Features to Improve Time Series Prediction

Author

Listed:
  • Borkin Dmitrii

    (Slovak University of Technology in Bratislava, Faculty of Materials Science and Technology in Trnava, Institute of Applied Informatics, Automation and Mechatronics, Ulica Jána Bottu Č. 2781/25, 917 24Trnava, Slovak Republic)

  • Németh Martin

    (Slovak University of Technology in Bratislava, Faculty of Materials Science and Technology in Trnava, Institute of Applied Informatics, Automation and Mechatronics, Ulica Jána Bottu Č. 2781/25, 917 24Trnava, Slovak Republic)

  • Michaľčonok German

    (Slovak University of Technology in Bratislava, Faculty of Materials Science and Technology in Trnava, Institute of Applied Informatics, Automation and Mechatronics, Ulica Jána Bottu Č. 2781/25, 917 24Trnava, Slovak Republic)

  • Mezentseva Olga

    (The Institute of Information Technologies and Telecommunications of NCFU355017Stavropol, St. Pushkin 1, Russian Federation)

Abstract

This paper aims at the time-series data analysis. We propose the possibility of adding additional features to the existing time series data set, to improve the prediction performance of the prediction model. The main goal of our research was to find a proper method for building a prediction model for the time-series data, using also machine learning methods. In this phase of research, we aim at the data analysis and proposal of the ways to add additional features to our dataset. In this paper, we aim at adding derived parameters from one of the original features. We also propose incorporating LAG’s into the dataset as new features, to enhance the prediction performance on the time series based data.

Suggested Citation

  • Borkin Dmitrii & Németh Martin & Michaľčonok German & Mezentseva Olga, 2019. "Adding Additional Features to Improve Time Series Prediction," Research Papers Faculty of Materials Science and Technology Slovak University of Technology, Sciendo, vol. 27(45), pages 72-78, September.
    • Handle: RePEc:vrs:repfms:v:27:y:2019:i:45:p:72-78:n:10
    DOI: 10.2478/rput-2019-0028
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