Genetic Algorithmised Neuro Fuzzy System for Forecasting the Online Journal Visitors
DOI:
https://doi.org/10.47839/ijc.20.2.2165Keywords:
Scientific Journal Online, Visitors, Genetic Algorithm (GA), Neural Fuzzy System (NFS), Root Mean Square Error (RMSE)Abstract
Artificial Neural Network (ANN) is recognized as one of effective forecasting engines for various business fields. This approach fits well with non-linear data. In fact, it is a black box system with random weighting, which is hard to train. One way to improve its performance is by hybridizing ANN with other methods. In this paper, a hybrid approach, Genetic Algorithm-Neural Fuzzy System (GA-NFS) is proposed to predict the number of unique visitors of an online journal website. The neural network weight is precisely determined using GA. Afterwards, the best weight has been used for testing data and processed using Sugeno Fuzzy Inference System (FIS) for time-series forecasting. Based on experiment, GA-NFS have been produced accuracy with 0.989 of root mean square error (RMSE) that is lower than the RMSE of a common NFS (2,004). This may indicate that the GA based weighting is able to improve the NFS performance on forecasting the number of journal unique visitors.
References
M. F. Iqbal, M. Zahid, D. Habib, and L. K. John, “Efficient prediction of network traffic for real-time applications,” Journal of Computer Networks and Communications, vol. 2019, p. 4067135, 2019, https://doi.org/10.1155/2019/4067135.
A. Subashini, S. K, S. Saranya, and U. Harsha, “Forecasting website traffic using prophet time series model,” International Research Journal of Multidisciplinary Technovation, vol. 1, no. 1 SE-Articles, pp. 56–63, 2019, https://doi.org/10.34256/irjmt1917.
T. Shelatkar, S. Tondale, S. Yadav, and S. Ahir, “Web traffic time series forecasting using ARIMA and LSTM RNN,” ITM Web of Conferences, vol. 32, pp. 3017, 2020, https://doi.org/10.1051/itmconf/20203203017.
N. Petluri and E. Al-Masri, “Web traffic prediction of Wikipedia pages,” Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), 2018, pp. 5427–5429, https://doi.org/10.1109/BigData.2018.8622207.
P. Purnawansyah, H. Haviluddin, R. Alfred, and A. F. O. Gaffar, “Network traffic time series performance analysis using statistical methods,” Knowledge Engineering and Data Science, vol. 1, no. 1, pp. 1–7, 2017, https://doi.org/10.17977/um018v1i12018p1-7.
P. Purnawansyah, H. Haviluddin, H. J. Setyadi, K. Wong, R. Alfred, and A. P. Wibawa, “An inflation rate prediction based on backpropagation neural network algorithm,” International Journal of Artificial Intelligence Research, vol. 3, no. 2, pp. 92–98, 2019, https://doi.org/10.29099/ijair.v3i2.112.
X. Feng, T. M. Fu, H. Cao, H. Tian, Q. Fan, and X. Chen, “Neural network predictions of pollutant emissions from open burning of crop residues: Application to air quality forecasts in southern China,” Atmospheric Environment, vol. 204, pp. 22–31, 2019, ttps://doi.org/10.1016/j.atmosenv.2019.02.002.
Z. Zhang, X. Wang, and Y. Ou, “Water simulation method based on BPNN response and analytic geometry,” Procedia Environmental Sciences, vol. 2, no. 5, pp. 446–453, 2010, https://doi.org/10.1016/j.proenv.2010.10.049.
C. N. Stefanakos and E. Vanem, “Nonstationary fuzzy forecasting of wind and wave climate in very long-term scales,” Journal of Ocean Engineering and Science, vol. 3, no. 2, pp. 144–155, 2018, https://doi.org/10.1016/j.joes.2018.04.001.
G. C. Silva, J. L. R. Silva, A. C. Lisboa, D. A. G. Vieira, and R. R. Saldanha, “Advanced fuzzy time series applied to short term load forecasting,” Proceedings of the 2017 IEEE Latin American Conference on Computational Intelligence, 2017, pp. 1–6, https://doi.org/10.1109/LA-CCI.2017.8285726.
D. Ali, M. Yohanna, M. I. Puwu, and B. M. Garkida, “Long-term load forecast modelling using a fuzzy logic approach,” Pacific Science Review A: Natural Science and Engineering, vol. 18, no. 2, pp. 123–127, 2016, https://doi.org/10.1016/j.psra.2016.09.011.
J. Nou, R. Chauvin, A. Traoré, S. Thil, and S. Grieu, “Atmospheric turbidity forecasting using side-by-side ANFIS,” Energy Procedia, vol. 49, pp. 2387–2397, 2014, https://doi.org/10.1016/j.egypro.2014.03.253.
Y. L. Zhang and J. H. Lei, “Prediction of laser cutting roughness in intelligent manufacturing mode based on ANFIS,” Procedia Engineering, vol. 174, pp. 82–89, 2017, https://doi.org/10.1016/j.proeng.2017.01.152.
M. Wu, C. Stefanakos, Z. Gao, and S. Haver, “Prediction of short-term wind and wave conditions for marine operations using a multi-step-ahead decomposition-ANFIS model and quantification of its uncertainty,” Ocean Engineering, vol. 188, p. 106300, 2019, https://doi.org/10.1016/j.oceaneng.2019.106300.
Y. Kassem and H. Çamur, “Prediction of biodiesel density for extended ranges of temperature and pressure using adaptive neuro-fuzzy inference system (ANFIS) and radial basis function (RBF),” Procedia Computer Science, vol. 120, no. 2017, pp. 311–316, 2017, https://doi.org/10.1016/j.procs.2017.11.244.
L. T. Zhao and G. R. Zeng, “Analysis of timeliness of oil price news information based on SVM,” Energy Procedia, vol. 158, pp. 4123–4128, 2019, dhttps://doi.org/10.1016/j.egypro.2019.01.821.
M. Malvoni, M. G. De Giorgi, and P. M. Congedo, “Data on support vector machines (SVM) model to forecast photovoltaic power,” Data in Brief, vol. 9, pp. 13–16, 2016, https://doi.org/10.1016/j.dib.2016.08.024.
L. Hulianytskyi and A. Pavlenko, “Development and analysis of genetic algorithm for time series forecasting problem,” International Journal, vol. 4, no. 1, pp. 13–29, 2015.
Y. K. Al-Douri, H. Al-Chalabi, and J. Lundberg, “Time series forecasting using a two-level multi-objective genetic algorithm: A case study of maintenance cost data for tunnel fans,” Proceedings of the ADVCOMP 2018 : The Twelfth International Conference on Advanced Engineering Computing and Applications in Sciences, 2018, pp. 4–9, https://doi.org/10.3390/a11080123.
N. H. J. Al-Saadi, “Forecasting traffic accidents according to ( types of roads and their causatives ) in Iraq using ARMA of low ordered combinations models,” International Journal of Engineering & Technology, vol. 8, no. 1.5, pp. 9–15, 2019.
E. Akdeniz, E. Egrioglu, E. Bas, and U. Yolcu, “An ARMA Type Pi-Sigma Artificial Neural Network for nonlinear time series forecasting,” Journal of Artificial Intelligence and Soft Computing Research, vol. 8, no. 2, pp. 121–132, 2018, https://doi.org/10.1515/jaiscr-2018-0009.
W. Wang, “Improved short term load forecasting of power system based on ARMA model,” Advances in Economics, Business and Management Research, vol. 30, pp. 12–19, 2016, https://doi.org/10.2991/iconfem-16.2016.2.
C. Liu, S. C. H. Hoi, P. Zhao, and J. Sun, “Online ARIMA algorithms for time series prediction,” Proceedings of the 30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016, pp. 1867–1873.
M. Milenković, L. Švadlenka, V. Melichar, N. Bojović, and Z. Avramović, “SARIMA modelling approach for railway passenger flow forecasting,” Transport, vol. 33, no. 5, pp. 1113–1120, 2018, https://doi.org/10.3846/16484142.2016.1139623.
R. Rossetti, “Forecasting the sales of console games for the Italian market,” Ekonometria, vol. 23, no. 3, pp. 76–88, 2019, https://doi.org/10.15611/eada.2019.3.07.
M. Shafaei, J. Adamowski, A. Fakheri-Fard, Y. Dinpashoh, and K. Adamowski, “A wavelet-SARIMA-ANN hybrid model for precipitation forecasting,” Journal of Water and Land Development, vol. 28, no. 1, pp. 27–36, 2016, https://doi.org/10.1515/jwld-2016-0003.
H. Ince and T. B. Trafalis, “A hybrid forecasting model for stock market prediction,” Economic Computation and Economic Cybernetics Studies and Research, vol. 51, no. 3, pp. 263–280, 2017.
M. Siddique, D. Panda, S. DAs, and S. K. Mohapatra, “A hybrid forecasting model for stock value prediction using soft computing skill,” International Journal of Computer Sciences and Engineering, vol. 117, no. 19, pp. 357–363, 2017.
C. Wu, P. Luo, Y. Li, L. Wang, and K. Chen, “Stock price forecasting: Hybrid model of artificial intelligent methods,” Engineering Economics, vol. 26, no. 1, pp. 40–48, 2015, https://doi.org/10.5755/j01.ee.26.1.3836.
H. Nazaripouya, B. Wang, Y. Wang, P. Chu, H. R. Pota, and R. Gadh, “Univariate time series prediction of solar power using a hybrid wavelet-ARMA-NARX prediction method,” Proceedings of the 2016 IEEE/PES Transmission and Distribution Conference and Exposition, 2016, pp. 1–5, https://doi.org/10.1109/TDC.2016.7519959.
R. M. K. T. Ratnayaka, D. M. K. N. Seneviratne, W. Jianguo, and H. I. Arumawadu, “A hybrid statistical approach for stock market forecasting based on artificial neural network and ARIMA time series models,” Proceedings of the 2015 International Conference on Behavioral, Economic and Socio-Cultural Computing, BESC 2015, 2015, pp. 54–60, dhttps://doi.org/10.1109/BESC.2015.7365958.
I. Khandelwal, R. Adhikari, and G. Verma, “Time series forecasting using hybrid arima and ann models based on DWT decomposition,” Procedia Computer Science, vol. 48, pp. 173–179, 2015, dhttps://doi.org/10.1016/j.procs.2015.04.167.
S. Panigrahi and H. S. Behera, “A hybrid ETS–ANN model for time series forecasting,” Engineering Applications of Artificial Intelligence, vol. 66, no. November 2019, pp. 49–59, 2017, https://doi.org/10.1016/j.engappai.2017.07.007.
Y. Liu, Y. Sun, D. Infield, Y. Zhao, S. Han, and J. Yan, “A hybrid forecasting method for wind power ramp based on Orthogonal Test and Support Vector Machine (OT-SVM),” IEEE Transactions on Sustainable Energy, vol. 8, no. 2, pp. 451–457, 2017, https://doi.org/10.1109/TSTE.2016.2604852.
N. R. Sari, A. P. Wibawa, and W. F. Mahmudy, “Comparison of ANFIS and NFS on inflation rate forecasting,” Proceedings of the 2017 5th International Conference on Electrical, Electronics and Information Engineering: Smart Innovations for Bridging Future Technologies, ICEEIE 2017, 2017, pp. 123-130, https://doi.org/10.1109/ICEEIE.2017.8328775.
D. Marcek, “Forecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statistics,” Complex & Intelligent Systems, vol. 4, no. 2, pp. 95–104, 2018, https://doi.org/10.1007/s40747-017-0056-6.
L. W. Yan, Z. Y. Sun, and K. Y. Mao, “Robust Optimization Based on an Improved Genetic Algorithm,” Advanced Materials Research, vol. 655–657, pp. 955–958, 2013, https://doi.org/10.4028/www.scientific.net/AMR.655-657.955.
N. R. Sari, W. F. Mahmudy, and A. P. Wibawa, “The effectiveness of hybrid backpropagation Neural Network model and TSK Fuzzy Inference System for inflation forecasting,” Journal of Telecommunication, Electronic and Computer Engineering, vol. 9, no. 2, pp. 111–117, 2017.
C. Tofallis, “A better measure of relative prediction accuracy for model selection and model estimation,” Journal of the Operational Research Society, vol. 66, no. 8, pp. 1352–1362, 2015, https://doi.org/10.1057/jors.2014.103.
T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature,” Geoscientific Model Development, vol. 7, no. 3, pp. 1247–1250, 2014, https://doi.org/10.5194/gmd-7-1247-2014.
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