Modeling and Forecasting Time-Series Data with Multiple Seasonal Periods Using Periodograms

Solomon Buke Chudo () and Gyorgy Terdik
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Solomon Buke Chudo: Doctoral School of Informatics, Program of Applied Information Technology and Its Theoretical Background, University of Debrecen, 4028 Debrecen, Hungary
Gyorgy Terdik: Department of Information Technology, Faculty of Informatics, University of Debrecen, 4028 Debrecen, Hungary

Econometrics, 2025, vol. 13, issue 2, 1-19

Abstract: Applications of high-frequency data, including energy management, economics, and finance, frequently require time-series forecasting characterized by complex seasonality. Recognizing prevailing seasonal trends continues to be difficult, given that the majority of solutions depend on basic decomposition techniques. This study introduces a new approach employing periodograms from spectral density analysis to identify predominant seasonal periods. When analyzing hourly electricity consumption data from Brazil, we identified three significant seasonal patterns: sub-daily (6 h), half-daily (12 h), and daily (24 h). We assessed the predictive efficacy of the BATS, TBATS, and STL + ETS models using these seasonal periods. We performed data analysis and model fitting in R 4.4.1 and used accuracy metrics like MAE, MAPE, and others to compare the models. The STL + ETS model exhibited an enhanced performance, surpassing both BATS and TBATS in energy forecasting. These findings improve our understanding of multiple seasonal patterns, assist us in selecting dominating periods, provide new practical forecasting approaches for time-series analysis, and inform professionals seeking superior forecasting solutions in various fields.

Keywords: time series; periodogram; energy; complex seasonality; forecasting; BATS; TBATS; STL models (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2025
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