Abstract
This paper follows the paper named Comparison of methods for time series data analysis for further use of machine learning algorithms. In previous paper we were dealing with the initial analysis of the time series data from a thermal plant. In this paper we aim at the design of a prediction model with the use of machine learning methods XGBoost and LightGBM. In the first part of this paper we focus at feature engineering where we add supplemental parameters to the existing dataset. In the second part of this paper we are dealing with the performance comparison of the mentioned machine-learning methods XGBoost and LightGBM.
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Acknowledgments
This publication is the result of implementation of the project: “UNIVERSITY SCIENTIFIC PARK: CAMPUS MTF STU - CAMBO” (ITMS: 26220220179) supported by the Research & Development Operational Program funded by the EFRR.
This publication is the result of implementation of the project VEGA 1/0673/15: “Knowledge discovery for hierarchical control of technological and production processes” supported by the VEGA.
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Nemeth, M., Borkin, D., Michalconok, G. (2019). The Comparison of Machine-Learning Methods XGBoost and LightGBM to Predict Energy Development. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Computational Statistics and Mathematical Modeling Methods in Intelligent Systems. CoMeSySo 2019 2019. Advances in Intelligent Systems and Computing, vol 1047. Springer, Cham. https://doi.org/10.1007/978-3-030-31362-3_21
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DOI: https://doi.org/10.1007/978-3-030-31362-3_21
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