Predictive Intelligence with Machine Learning Techniques for Complex Forecasting

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 fields, including (but 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 remain competitive in the market, and forecasting 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 the use of big data to improve the forecasting process, resulting in the development of forecasting methods and techniques. Traditional time series prediction methods are unable to handle the complex nonlinear relationship of a large data set. Most of the existing techniques are unable to manage multiple the dimensions of a data set, due to which, the computational complexity escalates with the increasing size of a data set. The prospect of achieving highly accurate and reliable forecasting has remained challenging.

Machine learning is a method used for forecasting in various fields. With machine learning, a system learns from 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 targets. 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 We invite interested authors to submit their original and unpublished work to this Special Issue.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:

• The 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;
• ML-based prediction for fault tolerance/testing for mobile multimedia computing.

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.

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