Special Issue "Emerging Topics in Data-Driven Forecasting Applications"

A special issue of Forecasting (ISSN 2571-9394).

Deadline for manuscript submissions: 1 February 2022.

Special Issue Editors

Dr. Daniele Apiletti
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Guest Editor
Department of Control and Computer Engineering, Politecnico di Torino, I-10129 Torino, Italy
Interests: data mining; data science; data analytics; industrial machine learning; data warehousing; NoSQL; big data
Dr. Loglisci Corrado
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Guest Editor
Department of Computer Science, University of Bari, 70125 Bari BA, Italy
Interests: data mining; machine learning; text mining
Prof. Dr. Tania Cerquitelli
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Guest Editor
Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy
Interests: data science; automated data analytics; transparent data mining; machine learning; text mining; concept drift methodologies; digital cities predictive maintenance
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Predicting the future has always been at the heart of human desire. Forecasting methods and techniques respond to this desire and have become a crucial asset in data-driven decision-making, from large enterprises and policy makers to private stakeholders and personal life.

The fast-paced digital world we live in provides us with the chance to dig from increasingly larger data collections and learn trends, patterns, and systemic behaviors, opening the way to address novel challenges in forecasting.

From data collection and preprocessing to model building, from theoretical contributions to cutting-edge applications, from machine learning to data visualization, successful forecasting solutions require a blend of skills and competences, opening many research issues, as also highlighted by recent events such as the Covid-19 emergency.

The goal of this Special Issue is to disseminate cutting-edge applied-research findings and real-world advances on innovative forecasting methodologies and technologies, by collecting new emerging forecasting solutions within the research community. Specifically, innovative contributions that advance the understanding of issues related to data-driven forecasting applications are welcome. We envision that such contributions could address different data-related issues, such as the heterogeneity of data types (e.g., in health-care applications), the complexity of data (e.g., complex networks), and different data formats (e.g., text and multimedia).

Interesting topics can also refer to machine-learning approaches to defining forecasting models in challenging data scenarios, such as those of semi-supervised learning and unsupervised learning, applied in many different domains, such as (but not only) economics and finance, energy, environment, industry, operations, and social good.

We invite the submission of high-quality manuscripts reporting relevant research contributions addressing various aspects of data-driven forecasting. Contributions to this Special Issue should be of interest to a large and varied cross-disciplinary audience of researchers and practitioners involved or interested in different aspects of this topic, following an open-science approach of making the research results accessible through open-access publication. The Special Issue welcomes submissions of technical, experimental, and methodological papers, application papers, open-data analysis, and papers on experience reports in real-life contexts.

Submissions of “extended versions” of already published works (e.g., conference/workshop papers/PhD theses) should be significantly extended with a relevant part of novel contribution. A brief “summary of differences” between the submitted paper to this Special Issue and the former one must be included. 

Dr. Daniele Apiletti
Dr. Loglisci Corrado
Prof. Dr. Tania Cerquitelli
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 papers will be 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 1000 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

  • prediction techniques
  • forecasting solutions
  • data-driven models
  • machine learning
  • data mining
  • open science research

Published Papers (1 paper)

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Research

Article
Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine
Forecasting 2021, 3(3), 460-477; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3030028 - 22 Jun 2021
Viewed by 540
Abstract
Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in [...] Read more.
Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts. Full article
(This article belongs to the Special Issue Emerging Topics in Data-Driven Forecasting Applications)
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