Special Issue "Improved Forecasting through Artificial Intelligence"

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

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editors

Dr. Niccolo Pescetelli
E-Mail Website
Guest Editor
D.Phil., Research scientist & Principal Investigator, Max Planck Institute, 94 Lentzeallee, 14195, Germany
Interests: hybrid intelligence; forecasting; collective behavior; decision making; computational social science
Mr. Gregg Willcox
E-Mail Website
Guest Editor
Director of Research and Development, Unanimous AI, 2443 Fillmore Street, San Francisco, CA 94115-1814, USA
Interests: forecasting; swarm AI; machine learning

Special Issue Information

Dear Colleagues,

We are pleased to announce a new Special Issue, entitled “Improved Forecasting through Artificial Intelligence.”

Artificial Intelligence (AI) is changing the way society operates, perceives, trades, and communicates. In the last decade, researchers in AI have made paradigm-shifting progresses, thanks to increasing data availability and lower computational costs. 

This Special Issue will cover recent advances in artificial intelligence to improve the ability of analysts, businesses, organizations, and intelligence agencies to accurately predict the future. 

We welcome submissions from researchers and practitioners in the field of forecasting that use algorithmic or hybrid methods. Priority will be given to research articles that present significant advances in their field of application, rather than incremental ones. 

We especially seek submissions that advance their field with original empirical findings, new methods, or designs. We also encourage researchers to submit insightful perspectives into the future challenges that current AI approaches are facing, social and methodological roadblocks, and moral considerations on the effect that artificial forecasting systems may have on consumers, citizens, and countries. 

Dr. Niccolo Pescetelli
Mr. Gregg Willcox
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

  • forecasting
  • artificial intelligence
  • predictive models
  • hybrid methods
  • algorithmic forecasting
  • financial forecasting
  • sport forecasting
  • geo-political forecasting
  • machine learning

Published Papers (2 papers)

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Article
Examining Deep Learning Architectures for Crime Classification and Prediction
Forecasting 2021, 3(4), 741-762; https://doi.org/10.3390/forecast3040046 - 12 Oct 2021
Viewed by 144
Abstract
In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms in this domain and provide recommendations for designing and training deep learning systems for predicting crime areas, using [...] Read more.
In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms in this domain and provide recommendations for designing and training deep learning systems for predicting crime areas, using open data from police reports. Having time-series of crime types per location as training data, a comparative study of 10 state-of-the-art methods against 3 different deep learning configurations is conducted. In our experiments with 5 publicly available datasets, we demonstrate that the deep learning-based methods consistently outperform the existing best-performing methods. Moreover, we evaluate the effectiveness of different parameters in the deep learning architectures and give insights for configuring them to achieve improved performance in crime classification and finally crime prediction. Full article
(This article belongs to the Special Issue Improved Forecasting through Artificial Intelligence)

Other

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Perspective
A Brief Taxonomy of Hybrid Intelligence
Forecasting 2021, 3(3), 633-643; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3030039 - 01 Sep 2021
Viewed by 331
Abstract
As artificial intelligence becomes ubiquitous in our lives, so do the opportunities to combine machine and human intelligence to obtain more accurate and more resilient prediction models across a wide range of domains. Hybrid intelligence can be designed in many ways, depending on [...] Read more.
As artificial intelligence becomes ubiquitous in our lives, so do the opportunities to combine machine and human intelligence to obtain more accurate and more resilient prediction models across a wide range of domains. Hybrid intelligence can be designed in many ways, depending on the role of the human and the algorithm in the hybrid system. This paper offers a brief taxonomy of hybrid intelligence, which describes possible relationships between human and machine intelligence for robust forecasting. In this taxonomy, biological intelligence represents one axis of variation, going from individual intelligence (one individual in isolation) to collective intelligence (several connected individuals). The second axis of variation represents increasingly sophisticated algorithms that can take into account more aspects of the forecasting system, from information to task to human problem-solvers. The novelty of the paper lies in the interpretation of recent studies in hybrid intelligence as precursors of a set of algorithms that are expected to be more prominent in the future. These algorithms promise to increase hybrid system’s resilience across a wide range of human errors and biases thanks to greater human-machine understanding. This work ends with a short overview for future research in this field. Full article
(This article belongs to the Special Issue Improved Forecasting through Artificial Intelligence)
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