Forecasting with Machine Learning Techniques

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Forecasting in Computer Science".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 32964

Special Issue Editors

Information Systems and Business Intelligence, Peter Faber Business School, Australian Catholic University, Sydney, NSW, Australia
Interests: artificial intelligence; machine learning; decision support system; Internet of Things; fuzzy systems
Special Issues, Collections and Topics in MDPI journals
Information Technology Department, College of Applied Sciences, University of Technology and Applied Sciences, P.O.Box 14, Postal Code 516, Ibri, Oman
Interests: service discovery; service forecasting; web harvesting; data analytics; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to investigate the use of Machine Learning (ML) techniques for forecasting as an alternative to the traditional techniques. Forecasting is an essential element of decision-making in various areas, including (but are not limited to) manufacturing, energy, supply chain management, and the environment. Forecasting is also widely practiced by businesses and organizations, as it helps them to plan for their needs and stay competitive in the market and is considered to be essential to setting strategies, resource requirements, future activities, etc. Recently, the big data revolution has influenced researchers to pay attention to using big data to improve the forecasting process, resulting in the development of forecasting methods and techniques. However, achieving highly accurate and reliable forecasting has remained challenging.

Machine learning is one of the methods used for forecasting in various fields. With machine learning, the system learns from the data in order to improve the analysis process and the accuracy of the prediction without human interference. Machine learning methods and algorithms include supervised, unsupervised, semi-supervised, and self-supervised methods that use intelligent strategies to find the target. The goal of this Special Issue is to focus on topics related to the use of machine learning techniques to solve forecasting problems in various fields. We invite interested authors to submit their original and unpublished work to this Special Issue.

Topics of interest to this Special Issue include, but are not limited to:

  • theoretical analysis of conformal prediction;
  • ML-based prediction applications for different fields, including business, healthcare, information systems, engineering, bioinformatics, and information security;
  • energy forecasting;
  • ML-based prediction for healthcare solutions;
  • prediction for resource management in supply chain management systems;
  • ML-based prediction for service management in the cloud/crowd marketplace;
  • ML-based prediction for resource allocation and scheduling; and
  • ML-based prediction for fault tolerance/testing for mobile multimedia computing.

Paper Submission:

Submitted papers should present original, unpublished work on topics related to forecasting that help different stakeholders solve industry problems. All submitted papers will be evaluated based on relevance, the significance of the contribution, technical quality, and quality of presentation by multiple independent reviewers (the articles will be reviewed following the journal’s standard peer-review procedures). We invite prospective authors to submit their manuscript via the online submission system on the journal’s main page. Please make sure you mention in your cover letter that you are submitting to this Special Issue.

We look forward to receiving your high-quality submissions.

Dr. Walayat Hussain
Dr. Asma Alkalbani
Prof. Dr. Honghao Gao
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. Forecasting is an international peer-reviewed open access quarterly 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.

Keywords

  • forecasting methodology
  • ML prediction applications
  • energy forecasting
  • management forecasting
  • environment forecasting
  • ML prediction for service management
  • ML prediction for healthcare

Published Papers (6 papers)

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Editorial

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2 pages, 190 KiB  
Editorial
Forecasting with Machine Learning Techniques
by Walayat Hussain, Asma Musabah Alkalbani and Honghao Gao
Forecasting 2021, 3(4), 868-869; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3040052 - 16 Nov 2021
Cited by 3 | Viewed by 2673
Abstract
The decision-maker is increasingly utilising machine learning (ML) techniques to find patterns in huge quantities of real-time data [...] Full article
(This article belongs to the Special Issue Forecasting with Machine Learning Techniques)

Research

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21 pages, 6424 KiB  
Article
Attention-Based CNN-RNN Arabic Text Recognition from Natural Scene Images
by Hanan Butt, Muhammad Raheel Raza, Muhammad Javed Ramzan, Muhammad Junaid Ali and Muhammad Haris
Forecasting 2021, 3(3), 520-540; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3030033 - 20 Jul 2021
Cited by 34 | Viewed by 4712
Abstract
According to statistics, there are 422 million speakers of the Arabic language. Islam is the second-largest religion in the world, and its followers constitute approximately 25% of the world’s population. Since the Holy Quran is in Arabic, nearly all Muslims understand the Arabic [...] Read more.
According to statistics, there are 422 million speakers of the Arabic language. Islam is the second-largest religion in the world, and its followers constitute approximately 25% of the world’s population. Since the Holy Quran is in Arabic, nearly all Muslims understand the Arabic language per some analytical information. Many countries have Arabic as their native and official language as well. In recent years, the number of internet users speaking the Arabic language has been increased, but there is very little work on it due to some complications. It is challenging to build a robust recognition system (RS) for cursive nature languages such as Arabic. These challenges become more complex if there are variations in text size, fonts, colors, orientation, lighting conditions, noise within a dataset, etc. To deal with them, deep learning models show noticeable results on data modeling and can handle large datasets. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can select good features and follow the sequential data learning technique. These two neural networks offer impressive results in many research areas such as text recognition, voice recognition, several tasks of Natural Language Processing (NLP), and others. This paper presents a CNN-RNN model with an attention mechanism for Arabic image text recognition. The model takes an input image and generates feature sequences through a CNN. These sequences are transferred to a bidirectional RNN to obtain feature sequences in order. The bidirectional RNN can miss some preprocessing of text segmentation. Therefore, a bidirectional RNN with an attention mechanism is used to generate output, enabling the model to select relevant information from the feature sequences. An attention mechanism implements end-to-end training through a standard backpropagation algorithm. Full article
(This article belongs to the Special Issue Forecasting with Machine Learning Techniques)
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22 pages, 1226 KiB  
Article
A New Machine Learning Forecasting Algorithm Based on Bivariate Copula Functions
by J. A. Carrillo, M. Nieto, J. F. Velez and D. Velez
Forecasting 2021, 3(2), 355-376; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3020023 - 27 May 2021
Cited by 5 | Viewed by 3898
Abstract
A novel forecasting method based on copula functions is proposed. It consists of an iterative algorithm in which a dependent variable is decomposed as a sum of error terms, where each one of them is estimated identifying the input variable which best “copulate” [...] Read more.
A novel forecasting method based on copula functions is proposed. It consists of an iterative algorithm in which a dependent variable is decomposed as a sum of error terms, where each one of them is estimated identifying the input variable which best “copulate” with it. The method has been tested over popular reference datasets, achieving competitive results in comparison with other well-known machine learning techniques. Full article
(This article belongs to the Special Issue Forecasting with Machine Learning Techniques)
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17 pages, 1254 KiB  
Article
Queue Length Forecasting in Complex Manufacturing Job Shops
by Marvin Carl May, Alexander Albers, Marc David Fischer, Florian Mayerhofer, Louis Schäfer and Gisela Lanza
Forecasting 2021, 3(2), 322-338; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3020021 - 11 May 2021
Cited by 8 | Viewed by 3698
Abstract
Currently, manufacturing is characterized by increasing complexity both on the technical and organizational levels. Thus, more complex and intelligent production control methods are developed in order to remain competitive and achieve operational excellence. Operations management described early on the influence among target metrics, [...] Read more.
Currently, manufacturing is characterized by increasing complexity both on the technical and organizational levels. Thus, more complex and intelligent production control methods are developed in order to remain competitive and achieve operational excellence. Operations management described early on the influence among target metrics, such as queuing times, queue length, and production speed. However, accurate predictions of queue lengths have long been overlooked as a means to better understanding manufacturing systems. In order to provide queue length forecasts, this paper introduced a methodology to identify queue lengths in retrospect based on transitional data, as well as a comparison of easy-to-deploy machine learning-based queue forecasting models. Forecasting, based on static data sets, as well as time series models can be shown to be successfully applied in an exemplary semiconductor case study. The main findings concluded that accurate queue length prediction, even with minimal available data, is feasible by applying a variety of techniques, which can enable further research and predictions. Full article
(This article belongs to the Special Issue Forecasting with Machine Learning Techniques)
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19 pages, 7983 KiB  
Article
Fighting Deepfakes Using Body Language Analysis
by Robail Yasrab, Wanqi Jiang and Adnan Riaz
Forecasting 2021, 3(2), 303-321; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3020020 - 28 Apr 2021
Cited by 9 | Viewed by 4331
Abstract
Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger [...] Read more.
Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepfake detection method using upper body language analysis. Specifically, a many-to-one LSTM network was designed and trained as a classification model for deepfake detection. Different models were trained by varying the hyperparameters to build a final model with benchmark accuracy. We achieved 94.39% accuracy on the deepfake test set. The experimental results showed that upper body language can effectively detect deepfakes. Full article
(This article belongs to the Special Issue Forecasting with Machine Learning Techniques)
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Review

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26 pages, 2817 KiB  
Review
Trends in Using IoT with Machine Learning in Health Prediction System
by Amani Aldahiri, Bashair Alrashed and Walayat Hussain
Forecasting 2021, 3(1), 181-206; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3010012 - 07 Mar 2021
Cited by 88 | Viewed by 11603
Abstract
Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT [...] Read more.
Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT to demystify hidden patterns in bulk data for optimal prediction and recommendation systems. Healthcare has embraced IoT and ML so that automated machines make medical records, predict disease diagnoses, and, most importantly, conduct real-time monitoring of patients. Individual ML algorithms perform differently on different datasets. Due to the predictive results varying, this might impact the overall results. The variation in prediction results looms large in the clinical decision-making process. Therefore, it is essential to understand the different ML algorithms used to handle IoT data in the healthcare sector. This article highlights well-known ML algorithms for classification and prediction and demonstrates how they have been used in the healthcare sector. The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data. In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings. Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data. The paper also provides some examples of IoT and machine learning to predict future healthcare system trends. Full article
(This article belongs to the Special Issue Forecasting with Machine Learning Techniques)
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