DOI: https://doi.org/10.14710/medstat.17.1.93-104

IMPLEMENTATION OF PROPHET IN AMERICAN ELECTRICITY FORECASTING WITH AND WITHOUT PARAMETER TUNING

*Winita Sulandari scopus publons  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret, Indonesia, 57126, Indonesia
Yudho Yudhanto  -  Informatics Engineering, Vocational School, Universitas Sebelas Maret, Surakarta, Indonesia, 57129, Indonesia
Riskhia Hapsari  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret, Indonesia, 57126, Indonesia
Monica Dini Wijayanti  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret, Indonesia, 57126, Indonesia
Hilman Ferdinandus Pardede  -  Research Center for Data and Information Sciences National, National Research and Innovation Agency, Bandung, Indonesia, Indonesia
Open Access Copyright (c) 2024 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Prophet is one of the machine learning approximation methods that accommodate trends, seasonality, and holiday impacts in time series data. Generally, the performance of machine learning models can be improved by implementing hyperparameter tuning. This study investigates whether hyperparameter tuning can improve the model's performance. To show its effectiveness, the Prophet model constructed by parameter tuning is compared to the one with fixed parameter values (namely the default model) for both the original series and the Box-Cox transformed series in terms of mean absolute percentage error (MAPE). Based on the experimental results of the twenty-four daily electricity load time series in American Electric Power (AEP). This shows that parameter tuning successfully reduces the MAPE of the default model in the range of about 3-8% for training data. However, there is no guarantee for testing data. Although, in some cases, parameter tuning can reduce the MAPE value of the default model by up to 38%, in other cases, it actually increases the MAPE of the default model by almost 15%. The experiments on testing data also show that models built from transformed data do not necessarily produce more accurate forecast values than those built from the original data.
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Keywords: Prophet; electricity load forecasting; Box-Cox transformation; holidays; parameter tuning.
Funding: Kemendikbudristek

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Language : EN
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