Forecasting Techniques for Univariate Time Series Data: Analysis and Practical Applications by Category
Year: 2025
Type: conference-paper
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.
Authors: Leonard Dervishi, Antonios Raptakis, Gerald Bieber
Institution: Fraunhofer Institute for Computer Graphics Research
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W7084061529