Benchmark Models in Time Series Forecasting

Dear Colleagues,

Forecasting practice has long been shaped by a simple but powerful insight: uncomplicated methods often perform best in real-world, out-of-sample settings. Across disciplines such as economics, finance, energy, and operations, benchmark approaches—such as naive forecasts, exponential smoothing, and low-order autoregressive models—have consistently demonstrated robustness, transparency, and scalability. These methods succeed not despite their simplicity, but because of it: they adapt more effectively to structural change, avoid overfitting, and remain usable in practice.

This Special Issue is motivated by the view that simple benchmark forecasting methods often perform well in practice and builds on recent developments in the literature on benchmark methods in univariate time series forecasting. Our aim is to bring together contributions that extend, refine, or apply benchmark methods in modern forecasting environments.

We welcome submissions including, but not limited to:

• The development of new benchmark methods or refinements of existing ones;
• Predictive complexity and model selection in simple forecasting frameworks;
• Comparative studies of benchmark versus complex models;
• Applications in finance, economics, energy, and operations;
• Robustness, structural change, and model stability;
• Computationally efficient and scalable forecasting techniques.

Both methodological contributions and empirical applications are encouraged.

The goal of this Special Issue is to collect original research and review papers that provide new insights into univariate time series forecasting, with emphasis on methods that balance theoretical soundness with practical relevance. The topic aligns closely with the journal’s scope, focusing on mathematically grounded approaches that are also interpretable, scalable, and operational.

We look forward to receiving your original research articles and reviews.

Dr. Foteini Kyriazi
Prof. Dr. Dimitrios D. Thomakos
Dr. Stelios Arvanitis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• benchmark models
• new methodologies
• forecasting
• simplicity
• parsimony