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Volume 11
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Proceeding Paper

Forecasting Techniques for Univariate Time Series Data: Analysis and Practical Applications by Category †

by
Leonard Dervishi
*,
Antonios Raptakis
and
Gerald Bieber
Fraunhofer-Institute for Computer Graphics Research IGD, Joachim-Jungius-Str. 11, 18059 Rostock, Germany
*
Author to whom correspondence should be addressed.
†
Presented at the 11th International Conference on Time Series and Forecasting, Canaria, Spain, 16–18 July 2025.
Comput. Sci. Math. Forum 2025, 11(1), 21; https://doi.org/10.3390/cmsf2025011021
Published: 11 August 2025
(This article belongs to the Proceedings of The 11th International Conference on Time Series and Forecasting)
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Abstract

Effective forecasting is vital in various domains as it supports informed decision-making and risk mitigation. This paper aims to improve the selection of appropriate forecasting methods for univariate time series. We propose a systematic categorization based on key characteristics, such as stationarity and seasonality and analyze well-known forecasting techniques suitable for each category. Additionally, we examine how forecasting horizons, the time periods for which forecasts are generated, affect method performance, thus addressing a significant gap in the existing literature. Our findings reveal that certain techniques excel in specific categories and demonstrate performance progression over time, indicating how they improve or decline relative to other techniques. By enhancing the understanding of method effectiveness across diverse time series characteristics, this research aims to guide professionals in making informed choices for their forecasting needs.
Keywords: forecasting; time series analysis; univariate time series; forecasting horizons forecasting; time series analysis; univariate time series; forecasting horizons

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MDPI and ACS Style

Dervishi, L.; Raptakis, A.; Bieber, G. Forecasting Techniques for Univariate Time Series Data: Analysis and Practical Applications by Category. Comput. Sci. Math. Forum 2025, 11, 21. https://doi.org/10.3390/cmsf2025011021

AMA Style

Dervishi L, Raptakis A, Bieber G. Forecasting Techniques for Univariate Time Series Data: Analysis and Practical Applications by Category. Computer Sciences & Mathematics Forum. 2025; 11(1):21. https://doi.org/10.3390/cmsf2025011021

Chicago/Turabian Style

Dervishi, Leonard, Antonios Raptakis, and Gerald Bieber. 2025. "Forecasting Techniques for Univariate Time Series Data: Analysis and Practical Applications by Category" Computer Sciences & Mathematics Forum 11, no. 1: 21. https://doi.org/10.3390/cmsf2025011021

APA Style

Dervishi, L., Raptakis, A., & Bieber, G. (2025). Forecasting Techniques for Univariate Time Series Data: Analysis and Practical Applications by Category. Computer Sciences & Mathematics Forum, 11(1), 21. https://doi.org/10.3390/cmsf2025011021

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