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Mathematics
Special Issues
Time Series Forecasting for Economic and Financial Phenomena
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Time Series Forecasting for Economic and Financial Phenomena

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 5439

Special Issue Editors

Dr. Sophia Daskalaki

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Patras, 26 504 Rio, Greece
Interests: operations research; probability and statistics; data mining
Dr. Christos Katris

E-Mail Website
Guest Editor
Department of Mathematics, University of Patras, 26 504 Rio, Greece
Interests: applied statistics; time series and forecasting; econometrics; quantitative finance; risk management

Special Issue Information

Dear Colleagues,

The significance of time series forecasting is undoubtedly present in many fields, with the financial and economic sectors being among them. For many financial and economic phenomena, prediction of the future is of crucial importance when making decisions. The further development of the theory, models, and procedures in this extremely active field can enrich our arsenal for coping with uncertainty and other implications from such phenomena. Each phenomenon is characterized by several statistical properties and models which incorporate such properties are continuously being developed. It has been noticed, however, that while traditional time series models are still valid choices, interdisciplinary approaches are attracting the interest of researchers.

This Special Issue seeks original articles on time series forecasting and its applications in the economy and financial markets. Innovative methodological approaches for modelling economic and financial phenomena, as well as interesting empirical applications, are welcome. The use of statistical methods and machine learning approaches are both of interest. The topics may include, but are not limited to, the development and application of time series models and methods for describing and forecasting phenomena such as unemployment, economic development, GDP, stock markets, commodity markets, betting markets, economics of happiness, social and solidarity economy, asset and derivative pricing, and risk management.

Dr. Sophia Daskalaki
Dr. Christos Katris
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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics 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 2600 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.

Keywords

  • time series models
  • time series econometrics
  • financial econometrics
  • application of time series to real-world problems
  • time series forecasting
  • time series analysis
  • machine learning in forecasting

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Published Papers (2 papers)

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15 pages, 366 KiB  
Article
Statistical Modeling to Improve Time Series Forecasting Using Machine Learning, Time Series, and Hybrid Models: A Case Study of Bitcoin Price Forecasting
by Moiz Qureshi, Hasnain Iftikhar, Paulo Canas Rodrigues, Mohd Ziaur Rehman and S. A. Atif Salar
Mathematics 2024, 12(23), 3666; https://doi.org/10.3390/math12233666 - 22 Nov 2024
Cited by 5 | Viewed by 2425
Abstract
Bitcoin (BTC-USD) is a virtual currency that has grown in popularity after its inception in 2008. BTC-USD is an internet communication network that makes using digital money, including digital payments, easy. It offers decentralized clearing of transactions and money supply. This study attempts [...] Read more.
Bitcoin (BTC-USD) is a virtual currency that has grown in popularity after its inception in 2008. BTC-USD is an internet communication network that makes using digital money, including digital payments, easy. It offers decentralized clearing of transactions and money supply. This study attempts to accurately anticipate the BTC-USD prices (Close) using data from September 2023 to September 2024, comprising 390 observations. Four machine learning models—Multi-layer Perceptron, Extreme Learning Machine, Neural Network AutoRegression, and Extreme-Gradient Boost—as well as four time series models—Auto-Regressive Integrated Moving Average, Auto-Regressive, Non-Parametric Auto-Regressive, and Simple Exponential Smoothing models—are used to achieve this end. Various hybrid models are then proposed utilizing these models, which are based on simple averaging of these models. The data-splitting technique, commonly used in comparative analysis, splits the data into training and testing data sets. Through comparison testing with training data sets consisting of 30%, 20%, and 10%, the present work demonstrated that the suggested hybrid model outperforms the individual approaches in terms of error metrics, such as the MAE, RMSE, MAPE, SMAPE, and direction accuracy, such as correlation and the MDA of BTC. Furthermore, the DM test is utilized in this study to measure the differences in model performance, and a graphical evaluation of the models is also provided. The practical implication of this study is that financial analysts have a tool (the proposed model) that can yield insightful information about potential investments. Full article
(This article belongs to the Special Issue Time Series Forecasting for Economic and Financial Phenomena)
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24 pages, 2468 KiB  
Article
An Inconvenient Truth about Forecast Combinations
by Pablo Pincheira-Brown, Andrea Bentancor and Nicolás Hardy
Mathematics 2023, 11(18), 3806; https://doi.org/10.3390/math11183806 - 5 Sep 2023
Cited by 1 | Viewed by 2005
Abstract
It is well-known that the weighted averages of two competing forecasts may reduce mean squared prediction errors (MSPE) and may also introduce certain inefficiencies. In this paper, we take an in-depth view of one particular type of inefficiency stemming from simple combination schemes: [...] Read more.
It is well-known that the weighted averages of two competing forecasts may reduce mean squared prediction errors (MSPE) and may also introduce certain inefficiencies. In this paper, we take an in-depth view of one particular type of inefficiency stemming from simple combination schemes: Mincer and Zarnowitz inefficiency or auto-inefficiency for short. Under mild assumptions, we show that linear convex forecast combinations are almost always auto-inefficient, and, therefore, greater reductions in MSPE are almost always possible. In particular, we show that the process of taking averages of forecasts may induce inefficiencies in the combination, even when individual forecasts are efficient. Furthermore, we show that the so-called “optimal weighted average” traditionally presented in the literature may indeed be inefficient as well. Finally, we illustrate our findings with simulations and an empirical application in the context of the combination of headline inflation forecasts for eight European economies. Overall, our results indicate that in situations in which a number of different forecasts are available, the combination of all of them should not be the last step taken in the search of forecast accuracy. Attempts to take advantage of potential inefficiencies stemming from the combination process should also be considered. Full article
(This article belongs to the Special Issue Time Series Forecasting for Economic and Financial Phenomena)
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