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Algorithms
Special Issues
Algorithms and Optimization Models for Forecasting and Prediction
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Algorithms and Optimization Models for Forecasting and Prediction

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (1 February 2023) | Viewed by 4410

Special Issue Editor

Dr. Ahmed A. Ewees

E-Mail Website
Guest Editor
Department of Computer, Damietta University, Damietta 34511, Egypt
Interests: computer vision; deep learning; data science; swarm intelligence; image segmentation; global optimization;
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Several artificial intelligence and machine learning (ML) approaches focus on how computers simulate human learning behaviors. ML contains different approaches such as supervised and unsupervised learning as well as various types of prediction, classification, and forecasting methods and algorithms. We invite you to submit your latest research in the area of algorithms and optimization methods to solve and improve different prediction and forecasting applications, including, but not limited to, time series forecasting, information retrieval, forecasting methods, remote sensing, computer vision, and pattern recognition.

Dr. Ahmed A. Ewees
Guest Editor

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. Algorithms is an international peer-reviewed open access monthly 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 1600 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

  • machine learning
  • forecasting
  • predictive models
  • time series optimization model
  • improved forecasting models
  • metaheuristic algorithms
  • hybrid methods
  • financial forecasting
  • machine learning

Published Papers (2 papers)

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Research

21 pages, 590 KiB  
Article
Conditional Temporal Aggregation for Time Series Forecasting Using Feature-Based Meta-Learning
by Anastasios Kaltsounis, Evangelos Spiliotis and Vassilios Assimakopoulos
Algorithms 2023, 16(4), 206; https://0-doi-org.brum.beds.ac.uk/10.3390/a16040206 - 12 Apr 2023
Viewed by 1661
Abstract
We present a machine learning approach for applying (multiple) temporal aggregation in time series forecasting settings. The method utilizes a classification model that can be used to either select the most appropriate temporal aggregation level for producing forecasts or to derive weights to [...] Read more.
We present a machine learning approach for applying (multiple) temporal aggregation in time series forecasting settings. The method utilizes a classification model that can be used to either select the most appropriate temporal aggregation level for producing forecasts or to derive weights to properly combine the forecasts generated at various levels. The classifier consists a meta-learner that correlates key time series features with forecasting accuracy, thus enabling a dynamic, data-driven selection or combination. Our experiments, conducted in two large data sets of slow- and fast-moving series, indicate that the proposed meta-learner can outperform standard forecasting approaches. Full article
(This article belongs to the Special Issue Algorithms and Optimization Models for Forecasting and Prediction)
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20 pages, 2433 KiB  
Article
A Scenario-Based Model Comparison for Short-Term Day-Ahead Electricity Prices in Times of Economic and Political Tension
by Denis E. Baskan, Daniel Meyer, Sebastian Mieck, Leonhard Faubel, Benjamin Klöpper, Nika Strem, Johannes A. Wagner and Jan J. Koltermann
Algorithms 2023, 16(4), 177; https://0-doi-org.brum.beds.ac.uk/10.3390/a16040177 - 24 Mar 2023
Cited by 2 | Viewed by 1934
Abstract
In recent years, energy prices have become increasingly volatile, making it more challenging to predict them accurately. This uncertain market trend behavior makes it harder for market participants, e.g., power plant dispatchers, to make reliable decisions. Machine learning (ML) has recently emerged as [...] Read more.
In recent years, energy prices have become increasingly volatile, making it more challenging to predict them accurately. This uncertain market trend behavior makes it harder for market participants, e.g., power plant dispatchers, to make reliable decisions. Machine learning (ML) has recently emerged as a powerful artificial intelligence (AI) technique to get reliable predictions in particularly volatile and unforeseeable situations. This development makes ML models an attractive complement to other approaches that require more extensive human modeling effort and assumptions about market mechanisms. This study investigates the application of machine and deep learning approaches to predict day-ahead electricity prices for a 7-day horizon on the German spot market to give power plants enough time to ramp up or down. A qualitative and quantitative analysis is conducted, assessing model performance concerning the forecast horizon and their robustness depending on the selected hyperparameters. For evaluation purposes, three test scenarios with different characteristics are manually chosen. Various models are trained, optimized, and compared with each other using common performance metrics. This study shows that deep learning models outperform tree-based and statistical models despite or because of the volatile energy prices. Full article
(This article belongs to the Special Issue Algorithms and Optimization Models for Forecasting and Prediction)
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