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Time Series Forecasting: A Hybrid Neuro-Fuzzy-Particle Swarm Optimization Model
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 40 |
| Year of Publication: 2020 |
| Authors: Muneer Abdullah Saeed Hazaa Al-Mekhlafy, Muneef Abdulkarim Farea Ahmed, Mohamed Ghaleb Yosuef |
10.5120/ijca2020920457
|
Muneer Abdullah Saeed Hazaa Al-Mekhlafy, Muneef Abdulkarim Farea Ahmed, Mohamed Ghaleb Yosuef . Time Series Forecasting: A Hybrid Neuro-Fuzzy-Particle Swarm Optimization Model. International Journal of Computer Applications. 176, 40 ( Jul 2020), 1-8. DOI=10.5120/ijca2020920457
Abstract
Forecasting is very important for planning and decision-making in all fields to forecast the conditions and cases surrounding the problem under study before making any decision[21]. Hence, many forecasting methods have been developed to produce accurate forecasting values, reduce the degree of randomness, the changes that affect the time series, and non-linearity of data. In this proposed research,A Hybrid model (Neuro-Fuzzy-PSO) to forecast Time series. The proposed Hybrid model in the first stage after data initialization generate fuzzy inference system(FIS) by NEURO-FUZZY, which use grid partition method, Fuzzy C-Mean (FCM), and subtractive clustering.In the second stage trains the model NEUROFUZZY by back propagation method, Hybrid method, and PSO method. The Revenue Tax data taken from the Republic Yemen during the period 2000-2014 as a data of time series to achieve, ministry of Finance. The performance of the proposed forecasting system is evaluated using common statistical standard measures such as Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and The linear repression. Also, the forecasting results obtained are compared with all models used. Experimental results showed that the hybrid model Neuro-Fuzzy-PSO of forecast reduces the degree of randomness, the changes that affect the time series, and non-linearity of data. The results for real data sets under consideration clearly prove that the hybrid model (generation by subtractive method and PSO training) is able to outperform each components model used separately in terms of increasing the forecasting accuracy and decreasing the overall forecasting errors.
References
- Ahmad Bagheri, Hamed Mohammadi Peyhani, and Mohsen Akbari. Financial forecasting using ANFIS networks with quantum-behaved particle swarm optimization. Expert Systems with Applications, 41(14):6235–6250, 2014.
- Sasan Barak and S Saeedeh Sadegh. Forecasting energy consumption using ensemble ARIMAANFIS hybrid algorithm. International Journal of Electrical Power Energy Systems, 82:92–104, 2016.
- Naveed Anwer Butt, Mian Muhammad Awais, and Qamar Abbas. Improved Diagnostic Accuracy in Dependent Personality Disorders: A Comparative Study of Neural Architectures and Hybrid Approaches on Functional Magnetic Resonance Imaging Data. Journal of Medical Imaging and Health Informatics, 9(4):697–705, 2019.
- Z. Ceylan, E. Pekel, S. Ceylan, and S. Bulkan. Biomass higher heating value prediction analysis by ANFIS, PSO-ANFIS and GA-ANFIS. Global Nest Journal, 20(3):589–597, 2018.
- Brad M Farrant, Janet Fletcher, and Murray T Maybery. Cognitive flexibility, theory of mind, and hyperactivity/ inattention. Child development research, 2014, 2014.
- Xinguang He, Huade Guan, and Jianxin Qin. A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall. Journal of Hydrology, 527(April 2019):88–100, 2015.
- Horng-I Hsieh, Tsung-Pei Lee, and Tian-Shyug Lee. A hybrid particle swarm optimization and support vector regression model for financial time series forecasting. International Journal of Business Administration, 2(2):48, 2011.
- Girish K Jha, Parimala Thulasiraman, and Ruppa K Thulasiram. PSO based neural network for time series forecasting. In 2009 International Joint Conference on Neural Networks, pages 1422–1427. IEEE, 2009.
- A Khosravi, R N N Koury, L Machado, and J J G Pabon. Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neurofuzzy inference system. Sustainable Energy Technologies and Assessments, 25:146–160, 2018.
- Maria Kitzig, Anton Kepic, and Ashley Grant. Near realtime classification of iron ore lithology by applying fuzzy inference systems to petrophysical downhole data. Minerals, 8(7):276, 2018.
- I. Hong Kuo, Shi Jinn Horng, Yuan Hsin Chen, Ray Shine Run, Tzong Wann Kao, Rong Jian Chen, Jui Lin Lai, and Tsung Lieh Lin. Forecasting TAIFEX based on fuzzy time series and particle swarm optimization. Expert Systems with Applications, 37(2):1494–1502, 2010.
- Massimo Panella and Antonio Stanislao Gallo. An inputoutput clustering approach to the synthesis of ANFIS networks. IEEE Transactions on fuzzy systems, 13(1):69–81, 2005.
- Victor H Quej, Javier Almorox, Javier A Arnaldo, and Laurel Saito. ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment. Journal of Atmospheric and Solar-Terrestrial Physics, 155:62–70, 2017.
- Hossein Javedani Sadaei, Rasul Enayatifar, Frederico Gadelha Guimartextbackslash aes, Maqsood Mahmud, and Zakarya A. Alzamil. Combining ARFIMA models and fuzzy time series for the forecast of long memory time series. Neurocomputing, 2016.
- Mohd Najib Mohd Salleh, Noureen Talpur, and Kashif Hussain Talpur. A modified neuro-fuzzy system using metaheuristic approaches for data classification. Artificial Intelligence: Emerging Trends and Applications, page 29, 2018.
- Georgios Sermpinis, Konstantinos Theofilatos, Andreas Karathanasopoulos, Efstratios F. Georgopoulos, and Christian Dunis. Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization. European Journal of Operational Research, 225(3):528–540, 2013.
- Matthew Settles. An introduction to particle swarm optimization. Department of Computer Science, University of Idaho, 2, 2005.
- Shahaboddin Shamshirband, Alireza Baghban, Masoud Hadipoor, and Amir Mosavi. Developing an ANFIS-PSO Model to Estimate 2 Mercury Emission in Combustion Flue Gases 3. (August), 2019.
- Kashif Hussain Talpur. Improvement of Fuzzy Neural Network Using Mine Blast Algorithm for Classification of Malaysian Small Medium Enterprises Based on Strength, 2015.
- Erman textbackslashcCakt and Waldemar Karwowski. Predicting the occurrence of adverse events using an adaptive neuro-fuzzy inference system (ANFIS) approach with the help of ANFIS input selection. Artificial Intelligence Review, 48(2):139–155, 2017.
- Jackie D Urrutia, Francis Leo T Mingo, and Ciandreu Noah M Balmaceda. Forecasting Income Tax Revenue of the Philippines Using Autoregressive Integrated Moving Average (Arima) Modeling: a Time Series Analysis. Am. Res. Thoughts, 1(9):1938–1992, 2015.
- Cyril Voyant, Gilles Notton, Soteris Kalogirou, Marie-Laure Nivet, Christophe Paoli, Fabrice Motte, and Alexis Fouilloy. Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105:569–582, 2017.
- R Ghasemy Yaghin, S M T Fatemi Ghomi, and S Ali Torabi. A possibilistic multiple objective pricing and lotsizing model with multiple demand classes. Fuzzy Sets and Systems, 231:26–44, 2013.
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