Time Series Forecasting Based on Support Vector Machine Using Particle Swarm Optimization


  • Zana Azeez Kakarash
  • Hawkar Saeed Ezat
  • Shokhan Ali Omar
  • Nawroz Fadhil Ahmed




Time Series, Support Vector Machine, Feature Extraction, Particle Swarm Optimization, hybrid approach


In recent years, due to the non-linear nature, complexity, and irregularity of time series, especially in energy consumption and climate, studying this field has become very important. Therefore, this study aims to provide a high accuracy and efficiency hybrid approach to time series forecasting. The proposed model is called EDFPSO-SVR (Empirical Mode Decomposition- Discrete Wavelet Transform- Feature selection with Particle Swarm Optimization-Support Vector Regression). In the proposed hybrid approach, the first step is to decompose the signal into the Intrinsic Mode Functions (IMF) component using the Empirical Mode Decomposition (EMD) algorithm. In the second step, each component is transformed into subsequences of approximation properties and details by converting the Wavelets. In the third step, the best feature is extracted by the PSO algorithm. The purpose of using the PSO algorithm is feature extraction and error minimization of the proposed approach. The fourth step, using time vector regression, has dealt with time series forecasting. Four data sets in two different fields have been used to evaluate the proposed method. The two datasets are electric load of England and Poland, and the other two datasets are related to the temperature of Australia and Belgium. Evaluation criteria include MSE, RMSE, MAPE, and MAE. The evaluation results of the proposed method with other Principal component analysis (PCA) feature extraction algorithms, and comparisons with methods and studies in this field, indicate the proper performance of the proposed approach.


L. F. S. Vilela, R. C. Leme, C. A. M. Pinheiro, O. A. S. Carpinteiro, “Forecasting financial series using clustering methods and support vector regression,” Artificial Intelligence Review, vol. 52, no. 2, pp. 743-773, 2019. https://doi.org/10.1007/s10462-018-9663-x.

Md. Karimuzzaman, Md. Moyazzem Hossain, “Forecasting performance of nonlinear time series models: an application to weather variable,” Springer, Modeling Earth Systems and Environment, vol. 6, no. 1, pp. 2451-2463, 2020. https://doi.org/10.1007/s40808-020-00826-6.

P. A. Owusu, S. Asumadu-Sarkodie, “A review of renewable energy sources, sustainability issues and climate change mitigation,” Cogent Engineering, Civil & Environmental Engineering, vol. 3, no. 1, pp. 1-14, 2016. https://doi.org/10.1080/23311916.2016.1167990.

M. Sabbir, A. Shourav, S. H. Shahidm, B. Singh, M. Mohsenipour, E. S. Chung, X. J. Wang, “Potential impact of climate change on residential energy consumption in Dhaka city,” Environmental Modeling & Assessment, vol. 23, no. 1, pp. 131-140, 2018. https://doi.org/10.1007/s10666-017-9571-5.

O. C. Ozerdem, E. O. Olaniyi, O. K. Oyedotun, “Short term load forecasting using particle swarm optimization neural network,” Proceedings of the 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception, ICSCCW 2017, vol. 120, no. 1, pp. 382-393, 2017. https://doi.org/10.1016/j.procs.2017.11.254.

M. A. Hammad, B. Jereb, B. Rosi, D. Dragan, “Methods and models for electric load forecasting: A comprehensive review,” Logistics & Sustainable Transport, vol. 11, no. 1, pp. 51-76, 2020. https://doi.org/10.2478/jlst-2020-0004.

R. K. Kaufmann, H. Kauppi, J. H. Stock, “Emissions, concentrations, & temperature: A time series analysis,” Climatic Change, vol. 77, no. 1, pp. 249-278, 2006. https://doi.org/10.1007/s10584-006-9062-1.

A. M. Pirbazari, M. Farmanbar, A. Chakravorty, C. H. Rong, “Short-term load forecasting using smart meter data: A generalization analysis,” MDPI, Processes, vol. 8, no. 4, pp. 484-504, 2020. https://doi.org/10.3390/pr8040484.

S. Ali, H. Mansoor, I. Khan, N. Arshad, “Short-term load forecasting using AMI data,” Electrical Engineering and Systems Science, Signal Processing, pp. 51-76, 2020. https://doi.org/10.1145/3307772.3330173.

T. Dimri, S. H. Ahmad, M. Sharif, “Time series analysis of climate variables using seasonal ARIMA approach,” Springer, Journal of Earth System Science, vol. 129, no. 1, pp. 129-165, 2020. https://doi.org/10.1007/s12040-020-01408-x.

N. F. Mohd Radzuan, Z. Othman, A. Abu Bakar, “Uncertain time series in weather prediction,” Proceedings of the 4th International Conference on Electrical Engineering and Informatics (ICEEI 2013), vol. 11, no. 1, pp. 557-564, 2013. https://doi.org/10.1016/j.protcy.2013.12.228.

Ch. Subaar, N. Apor, J. J. Fletcher, R. Galyuon, G. Eduse, V. W. Adayira, “Time series analysis for prediction of meteorological data from Wa, upper west region of Ghana,” Journal of Climatology & Weather Forecasting, vol. 6, no. 3, pp. 1-6, 2018. https://doi.org/10.4172/2332-2594.1000237.

J. M. Jimenez, L. Stokes, C. H. Moss, Q. Yang, V. N. Livina, “Modelling energy demand response using long short-term memory neural networks,” Energy Efficiency, vol. 13, no. 3, pp. 1263-1280, 2020. https://doi.org/10.1007/s12053-020-09879-z.

H. Al-Shaikh, A. Rahman, A. Zubair, “Electric load forecasting with hourly precision using long short-term memory networks,” Proceedings of the IEEE, 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1-6, 2019.

J. Zheng, C. Xu, Z. Zhang, X. Li, “Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network,” Proceedings of the 2017 51st IEEE Annual Conference on Information Sciences and Systems (CISS), pp. 1-6, 2017.

Y. Yaslan, B. Bican, “Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting,” Measurement, vol. 103, no. 1, pp. 1-26, 2017. https://doi.org/10.1016/j.measurement.2017.02.007.

M. Imani, “Long short-term memory network and support vector regression for electrical load forecasting,” Proceedings of the 2019 IEEE International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET), 2019, pp. 1-6. https://doi.org/10.1109/PGSRET.2019.8882730.

X. Xu, W. Ren, “A hybrid model based on a two-layer decomposition approach and an optimized neural network for chaotic time series prediction,” MDPI, Symmetry, vol. 11, no. 5, pp. 610-627, 2019. https://doi.org/10.3390/sym11050610.

L. M. Candanedo, V. Feldheim, D. Deramaix, “Data driven prediction models of energy use of appliances in a low-energy house,” Energy and Buildings, vol. 140, no. 1, pp. 81-97, 2017. https://doi.org/10.1016/j.enbuild.2017.01.083.

L. H. Tang, Y. L. Bai, J. Yang, Y. N. Lu, “A hybrid prediction method based on empirical mode decomposition and multiple model fusion for chaotic time series,” Chaos, Solitons and Fractals Nonlinear Science, and Nonequilibrium and Complex Phenomena, vol. 141, no. 1, pp. 1-12, 2020. https://doi.org/10.1016/j.chaos.2020.110366.

Y. Hu, J. Li, M. Hong, J. Ren, R. Lin, Y. Liu, “Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm: A case study of papermaking process,” Energy, vol. 170, no. 1, pp. 1215-1227, 2019. https://doi.org/10.1016/j.energy.2018.12.208.

S. Bahrami, R. A. Hooshmand, M. Parastegari, “Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm,” Energy, vol. 72, no. 1, pp. 434-442, 2014. https://doi.org/10.1016/j.energy.2014.05.065.

Y. Shen, J. Zhang, J. Liu, P. Zhan, R. Chen, Y. Chen, “Short-term load forecasting of power system based on similar day method and PSO-DBN,” Proceedings of the 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), pp. 1-6, 2018. https://doi.org/10.1109/EI2.2018.8582143.

H. Lu, M. Azimi, T. Iseley, “Short term load forecasting of urban gas using a hybrid model based on improved fruit fly optimization algorithm and support vector machine,” Energy Reports, vol. 5, no. 1, pp. 666-677, 2019. https://doi.org/10.1016/j.egyr.2019.06.003.

E. E. Elattar, N. A. Sabiha, M. Alsharef, M. K. Metwaly, “Short term electric load forecasting using hybrid algorithm for smart cities,” Springer, Science+Business Media, vol. 50, no. 1, pp. 3379-3399, 2020. https://doi.org/10.1007/s10489-020-01728-x.

B. Cannas, A. Fanni, L. See, G. Sias, “Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning,” Physics and Chemistry of the Earth, Parts A/B/C. Vol. 31, no. 1, pp. 1164-1171, 2006. https://doi.org/10.1016/j.pce.2006.03.020.

N. I. Sapankevych, R. Sankar, “Time series prediction using support vector machine: A survey,” IEEE, Computational Intelligence Magazine, vol. 4, no. 2, pp. 24-38, 2009. https://doi.org/10.1109/MCI.2009.932254.





S. Pourbahrami, M. A. Balafar, L. M. Khanli, Z. A. Kakarash, “A survey of neighborhood construction algorithms for clustering and classifying data points,” Computer Science Review, vol. 38, pp. 100315, 2020. https://doi.org/10.1016/j.cosrev.2020.100315.




How to Cite

Kakarash, Z. A., Ezat, H. S., Omar, S. A., & Ahmed, N. F. (2022). Time Series Forecasting Based on Support Vector Machine Using Particle Swarm Optimization. International Journal of Computing, 21(1), 76-88. https://doi.org/10.47839/ijc.21.1.2520