Applications of Forecasting by Hybrid Artificial Intelligent Technologies

Dear Colleagues,

Applications of hybridizing artificial intelligent technologies have been widely explored to address the complicated and nonlinear relationships among forecasting targets and other relevant factors. More accurate, or more precise, forecasts are required for decisions making in competitive environments. The development of hybrid artificial intelligent technologies would strongly support experts in any forecasting field to improve accuracy. In addition, this is of special relevance in the big data era, these data usually have dynamic, nonlinear complicate characteristics. Therefore, the forecasting models have often resulted in over-reliance on the use of informal judgments and higher expenses if lacking the ability to determine the data pattern. The novel applications of hybrid artificially intelligent technologies can provide more satisfactory performances.

This Special Issue aims to attract both academic researchers and practitioners from a wide range of forecasting fields, from engineering, operations research, economic, and also management. The so-called hybrid model means that some process of the former model is integrated into the process of the later one, for example, hybrid A and B implies some processes of A are controlled by A, some by B. Based on the definition of hybrid model, specifically, we are interested in contributions towards the development of all artificial intelligent technologies hybridizing with each other (as shown in the second keyword), or hybridizing novel intelligent tools with existing algorithms or existing models to improve or overcome the embedded drawbacks (as shown in the fourth keyword), or hybridizing with other novel methods, such as chaos theory, fuzzy theory, cloud theory, quantum behavior, and so on (as shown in the fifth and eighth keywords) to significantly improve forecasting accuracy.

All submissions should be based on the applications of the mentioned approaches and the developed models should also be presented with a corresponding theoretical sound framework, lacking such a scientific approach is discouraged. Validation support of existing/presented approaches is encouraged to be done using real practical applications.

Prof. Dr. Wei-Chiang Hong
Assoc. Prof. Dr. Ming-Wei Li
Dr. Yi Liang
Dr. Guo-Feng Fan
Guest Editors

Manuscript Submission Information

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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.

1. Statistical forecasting models (ARIMA; SARIMA; ARMAX; multi-variate regression; Kalman filter; exponential smoothing; and so on);
2. Hybrid evolutionary algorithms (including genetic algorithm, simulated annealing algorithm, particle swarm optimization, ant colony optimization, immune algorithm, artificial bee colony algorithm, etc.) in forecasting applications;
3. Theoretical comparison and empirical comparison of hybrid evolutionary algorithms and original evolutionary algorithms in forecasting applications;
4. Hybridizing chaotic mapping functions (including logistic mapping, cat mapping, Tent mapping, and An mapping, etc.) with evolutionary algorithms or forecasting models in forecasting applications;
5. Hybridizing fuzzy theory and fuzzy inference systems with evolutionary algorithms or forecasting models in forecasting applications;
6. Hybridizing artificial neural networks with evolutionary algorithms or forecasting models in forecasting applications;
7. Hybridizing knowledge-based expert systems with evolutionary algorithms or forecasting models in forecasting applications;
8. Hybridizing novel intelligent technologies (including wavelet transform, chaos theory, cloud theory, quantum theory) with evolutionary algorithms or forecasting models in forecasting applications.