Applications of Forecasting by Hybrid Artificial Intelligent Technologies

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

Deadline for manuscript submissions: closed (31 December 2018) | Viewed by 12717

Special Issue Editors


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Department of Information Management, Asia Eastern University of Science and Technology, Taipei 22064, Taiwan
Interests: short-term load forecasting; intelligent forecasting technologies (e.g., neural networks, knowledge–based expert systems, fuzzy inference systems, evolutionary computation, etc.); hybrid forecasting models (e.g., hybridizing traditional models with intelligent technologies, or hybridizing two or more different models to form a novel forecasting model); novel intelligent methodologies (chaos theory; cloud theory; quantum theory)
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College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China
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Guest Editor
College of Mathematics & Statistics, Pingdingshan University, Henan 467000, China

Special Issue Information

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

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Keywords

  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.

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Published Papers (2 papers)

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Research

14 pages, 1747 KiB  
Article
Fast Univariate Time Series Prediction of Solar Power for Real-Time Control of Energy Storage System
by Mostafa Majidpour, Hamidreza Nazaripouya, Peter Chu, Hemanshu R. Pota and Rajit Gadh
Forecasting 2019, 1(1), 107-120; https://doi.org/10.3390/forecast1010008 - 17 Sep 2018
Cited by 25 | Viewed by 6518
Abstract
In this paper, super-short-term prediction of solar power generation for applications in dynamic control of energy system has been investigated. In order to follow and satisfy the dynamics of the controller, the deployed prediction method should have a fast response time. To this [...] Read more.
In this paper, super-short-term prediction of solar power generation for applications in dynamic control of energy system has been investigated. In order to follow and satisfy the dynamics of the controller, the deployed prediction method should have a fast response time. To this end, this paper proposes fast prediction methods to provide the control system with one step ahead of solar power generation. The proposed methods are based on univariate time series prediction. That is, instead of using external data such as the weather forecast as the input of prediction algorithms, they solely rely on past values of solar power data, hence lowering the volume and acquisition time of input data. In addition, the selected algorithms are able to generate the forecast output in less than a second. The proposed methods in this paper are grounded on four well-known prediction algorithms including Autoregressive Integrated Moving Average (ARIMA), K-Nearest Neighbors (kNN), Support Vector Regression (SVR), and Random Forest (RF). The speed and accuracy of the proposed algorithms have been compared based on two different error measures, Mean Absolute Error (MAE) and Symmetric Mean Absolute Percentage Error (SMAPE). Real world data collected from the PV installation at the University of California, Riverside (UCR) are used for prediction purposes. The results show that kNN and RF have better predicting performance with respect to SMAPE and MAE criteria. Full article
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17 pages, 1398 KiB  
Article
Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming
by Marcos Álvarez-Díaz, Manuel González-Gómez and María Soledad Otero-Giráldez
Forecasting 2019, 1(1), 90-106; https://doi.org/10.3390/forecast1010007 - 13 Sep 2018
Cited by 11 | Viewed by 5446
Abstract
This study explores the forecasting ability of two powerful non-linear computational methods: artificial neural networks and genetic programming. We use as a case of study the monthly international tourism demand in Spain, approximated by the number of tourist arrivals and of overnight stays. [...] Read more.
This study explores the forecasting ability of two powerful non-linear computational methods: artificial neural networks and genetic programming. We use as a case of study the monthly international tourism demand in Spain, approximated by the number of tourist arrivals and of overnight stays. The forecasting results reveal that non-linear methods achieve slightly better predictions than those obtained by a traditional forecasting technique, the seasonal autoregressive integrated moving average (SARIMA) approach. This slight forecasting improvement was close to being statistically significant. Forecasters must judge whether the high cost of implementing these computational methods is worthwhile. Full article
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