Adding Additional Features to Improve Time Series Prediction

Borkin Dmitrii (), Németh Martin (), Michaľčonok German () and Mezentseva Olga ()
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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

Research Papers Faculty of Materials Science and Technology Slovak University of Technology, 2019, vol. 27, issue 45, 72-78

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.

Keywords: Time series; Prediction; Machine learning; Statistics (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:repfms:v:27:y:2019:i:45:p:72-78:n:10

DOI: 10.2478/rput-2019-0028

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