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Forecasting
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Improved Forecasting through Artificial Intelligence
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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: closed (31 December 2022) | Viewed by 44094

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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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20 pages, 13188 KiB  
Article
Evaluating State-of-the-Art, Forecasting Ensembles and Meta-Learning Strategies for Model Fusion
by Pieter Cawood and Terence Van Zyl
Forecasting 2022, 4(3), 732-751; https://doi.org/10.3390/forecast4030040 - 18 Aug 2022
Cited by 13 | Viewed by 4699
Abstract
The techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigates combining these two promising approaches, this paper focuses on the utility of the Exponential Smoothing-Recurrent Neural Network (ES-RNN) [...] Read more.
The techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigates combining these two promising approaches, this paper focuses on the utility of the Exponential Smoothing-Recurrent Neural Network (ES-RNN) in the pool of base learners for different ensembles. We compare against some state-of-the-art ensembling techniques and arithmetic model averaging as a benchmark. We experiment with the M4 forecasting dataset of 100,000 time-series, and the results show that the Feature-Based FORecast Model Averaging (FFORMA), on average, is the best technique for late data fusion with the ES-RNN. However, considering the M4’s Daily subset of data, stacking was the only successful ensemble at dealing with the case where all base learner performances were similar. Our experimental results indicate that we attain state-of-the-art forecasting results compared to Neural Basis Expansion Analysis (N-BEATS) as a benchmark. We conclude that model averaging is a more robust ensembling technique than model selection and stacking strategies. Further, the results show that gradient boosting is superior for implementing ensemble learning strategies. Full article
(This article belongs to the Special Issue Improved Forecasting through Artificial Intelligence)
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17 pages, 4376 KiB  
Article
Deep Learning for Demand Forecasting in the Fashion and Apparel Retail Industry
by Chandadevi Giri and Yan Chen
Forecasting 2022, 4(2), 565-581; https://doi.org/10.3390/forecast4020031 - 20 Jun 2022
Cited by 35 | Viewed by 22540
Abstract
Compared to other industries, fashion apparel retail faces many challenges in predicting future demand for its products with a high degree of precision. Fashion products’ short life cycle, insufficient historical information, highly uncertain market demand, and periodic seasonal trends necessitate the use of [...] Read more.
Compared to other industries, fashion apparel retail faces many challenges in predicting future demand for its products with a high degree of precision. Fashion products’ short life cycle, insufficient historical information, highly uncertain market demand, and periodic seasonal trends necessitate the use of models that can contribute to the efficient forecasting of products’ sales and demand. Many researchers have tried to address this problem using conventional forecasting models that predict future demands using historical sales information. While these models predict product demand with fair to moderate accuracy based on previously sold stock, they cannot fully be used for predicting future demands due to the transient behaviour of the fashion industry. This paper proposes an intelligent forecasting system that combines image feature attributes of clothes along with its sales data to predict future demands. The data used for this empirical study is from a European fashion retailer, and it mainly contains sales information on apparel items and their images. The proposed forecast model is built using machine learning and deep learning techniques, which extract essential features of the product images. The model predicts weekly sales of new fashion apparel by finding its best match in the clusters of products that we created using machine learning clustering based on products’ sales profiles and image similarity. The results demonstrated that the performance of our proposed forecast model on the tested or test items is promising, and this model could be effectively used to solve forecasting problems. Full article
(This article belongs to the Special Issue Improved Forecasting through Artificial Intelligence)
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10 pages, 1850 KiB  
Article
Prediction of Autonomy Loss in Alzheimer’s Disease
by Anne-Sophie Nicolas, Michel Ducher, Laurent Bourguignon, Virginie Dauphinot and Pierre Krolak-Salmon
Forecasting 2022, 4(1), 26-35; https://doi.org/10.3390/forecast4010002 - 27 Dec 2021
Cited by 1 | Viewed by 3107
Abstract
The evolution of functional autonomy loss leads to institutionalization of people affected by Alzheimer’s disease (AD), to an alteration of their quality of life and that of their caregivers. To predict loss of functional autonomy could optimize prevention strategies, aids and cost of [...] Read more.
The evolution of functional autonomy loss leads to institutionalization of people affected by Alzheimer’s disease (AD), to an alteration of their quality of life and that of their caregivers. To predict loss of functional autonomy could optimize prevention strategies, aids and cost of care. The aim of this study was to develop and to cross-validate a model to predict loss of functional autonomy as assessed by Instrumental Activities of Daily Living (IADL) score. Outpatients with probable AD and with 2 or more visits to the Clinical and Research Memory Centre of the University Hospital were included. Four Tree-Augmented Naïve bayesian networks (6, 12, 18 and 24 months of follow-up) were built. Variables included in the model were demographic data, IADL score, MMSE score, comorbidities, drug prescription (psychotropics and AD-specific drugs). A 10-fold cross-validation was conducted to evaluate robustness of models. The study initially included 485 patients in the prospective cohort. The best performance after 10-fold cross-validation was obtained with the model able to predict loss of functional autonomy at 18 months (area under the curve of the receiving operator characteristic curve = 0.741, 27% of patients misclassified, positive predictive value = 77% and negative predictive value = 73%). The 13 variables used explain 41.6% of the evolution of functional autonomy at 18 months. A high-performing predictive model of AD evolution of functional autonomy was obtained. An external validation is needed to use the model in clinical routine so as to optimize the patient care. Full article
(This article belongs to the Special Issue Improved Forecasting through Artificial Intelligence)
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22 pages, 3372 KiB  
Article
Examining Deep Learning Architectures for Crime Classification and Prediction
by Panagiotis Stalidis, Theodoros Semertzidis and Petros Daras
Forecasting 2021, 3(4), 741-762; https://doi.org/10.3390/forecast3040046 - 12 Oct 2021
Cited by 29 | Viewed by 7157
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)
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11 pages, 2175 KiB  
Perspective
A Brief Taxonomy of Hybrid Intelligence
by Niccolo Pescetelli
Forecasting 2021, 3(3), 633-643; https://doi.org/10.3390/forecast3030039 - 1 Sep 2021
Cited by 8 | Viewed by 4471
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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