SMOOTHING IN NEURAL NETWORK FOR UNIVARIAT TIME SERIES DATA FORECASTING
Keywords: smoothing, Univariate, Time Series, Neural Network
Time series data is interesting research material for many people. Not a few models have been produced, but very optimal accuracy has not been obtained. Neural network is one that is widely used because of its ability to understand non-linear relationships between data. This study will combine a neural network with exponential smoothing to produce higher accuracy. Exponential smoothing is one of the best linear methods is used for data set transformation and thereafter the new data set will be used in training and testing the Neural Network model. The resulting model will be evaluated using the standard error measure Root Mean Square Error (RMSE). Each model was compared with its RMSE value and then performed a T-Test. The proposed ES-NN model proved to have better predictive results than using only one method.
Aboagye-Sarfo, P., Mai, Q., Sanfilippo, F. M., Preen, D. B., Stewart, L. M., & Fatovich, D. M. (2015). A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia. Journal of Biomedical Informatics, 57, 62–73. https://doi.org/10.1016/j.jbi.2015.06.022
Airlangga, G., Rachmat, A., & Lapihu, D. (2019). Comparison of exponential smoothing and neural network method to forecast rice production in Indonesia. Telkomnika (Telecommunication Computing Electronics and Control), 17(3), 1367–1375. https://doi.org/10.12928/TELKOMNIKA.V17I3.11768
Aishwarya, D. C., & Babu, C. N. (2017). Prediction of time series data using GA-BPNN based hybrid ANN model. Proceedings - 7th IEEE International Advanced Computing Conference, IACC 2017, 848–853. https://doi.org/10.1109/IACC.2017.0174
Al-Douri, Y. K., Hamodi, H., & Lundberg, J. (2018). Time series forecasting using a two-level multi-objective genetic algorithm: A case study of maintenance cost data for tunnel fans. Algorithms, 11(8), 4–9. https://doi.org/10.3390/a11080123
Arevalo, A. (2016). Short-Term Forecasting of Financial Time Series with Deep Neural Networks, 42. Retrieved from http://www.bdigital.unal.edu.co/54538/
Astray, G., Mejuto, J. C., Martínez-Martínez, V., Nevares, I., Alamo-Sanza, M., & Simal-Gandara, J. (2019). Prediction models to control aging time in red wine. Molecules, 24(5). https://doi.org/10.3390/molecules24050826
Athanasopoulos, G., Song, H., & Sun, J. A. (2017). Bagging in Tourism Demand Modeling and Forecasting. Journal of Travel Research, 1–17. https://doi.org/10.1177/0047287516682871
Chuentawat, R., & Kan-Ngan, Y. (2019). The comparison of PM2.5 forecasting methods in the form of multivariate and univariate time series based on support vector machine and genetic algorithm. ECTI-CON 2018 - 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 572–575. https://doi.org/10.1109/ECTICon.2018.8619867
Essien, A. (2019). A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders. 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), 1–6.
Fajriyah, R., Asfah, I., Teknik, F., Kartini, U. T., Teknik, F., Surabaya, U. N., … Error, P. (2019). Peramalan Radiasi Global Matahari Jangka Pendek Menggunakan ModelTriple Exponential Smoothing-Feed Forward Neural Network, 677–684.
Faloutsos, C., Flunkert, V., Gasthaus, J., Januschowski, T., & Wang, Y. (2019). Forecasting big time series: Theory and practice. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2309–2310. https://doi.org/10.1145/3292500.3332289
Ferbar Tratar, L., Mojškerc, B., & Toman, A. (2016). Demand forecasting with four-parameter exponential smoothing. International Journal of Production Economics, 181, 162–173. https://doi.org/10.1016/j.ijpe.2016.08.004
Fischer, J. A., Pohl, P., & Ratz, D. (2020). A machine learning approach to univariate time series forecasting of quarterly earnings. Review of Quantitative Finance and Accounting, (0123456789). https://doi.org/10.1007/s11156-020-00871-3
Goswami, K., Ganguly, A., & Kumar Sil, A. (2019). Comparing univariate and multivariate methods for short term load forecasting. 2018 International Conference on Computing, Power and Communication Technologies, GUCON 2018, 972–976. https://doi.org/10.1109/GUCON.2018.8675059
Gunaryati, A., Kasyfi, F., & Andryana, S. (2019). HYBRID EXPONENTIAL SMOOTHING NEURAL NETWORK UNTUK HYBRID EXPONENTIAL SMOOTHING NEURAL NETWORK UNTUK PERAMALAN DATA PENGGUNA PITA, (August).
Hassani, H., Rua, A., Silva, E. S., & Thomakos, D. (2019). Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis. International Journal of Forecasting, 35(4), 1263–1272. https://doi.org/10.1016/j.ijforecast.2019.03.021
He, J. (2019). Ultra-short-term wind speed forecasting based on support vector machine with combined kernel function and similar data, 1–7.
Hu, Y., Xia, X., Fang, J., Ding, Y., Jiang, W., & Zhang, N. (2018). A multivariate regression load forecasting algorithm based on variable accuracy feedback. Energy Procedia, 152, 1152–1157. https://doi.org/10.1016/j.egypro.2018.09.147
Iwok, I. A., & Okpe, A. S. (2016). A Comparative Study between Univariate and Multivariate Linear Stationary Time Series Models. American Journal of Mathematics and Statistics, 6(5), 203–212. https://doi.org/10.5923/j.ajms.20160605.02
Journal, I., & Engineering, C. (2016). Hybrid Irradiation Forecasting Method Using Neural Network To Reduce Exponential Smoothing Error Mehryar Parsi, 13(5), 46–51. https://doi.org/10.9790/1684-1305034651
Koutlis, C., Papadopoulos, S., Schinas, M., & Kompatsiaris, I. (2020). LAVARNET: Neural network modeling of causal variable relationships for multivariate time series forecasting. Applied Soft Computing Journal, 96, 106685. https://doi.org/10.1016/j.asoc.2020.106685
Maciel, L., & Ballini, R. (2017). Interval fuzzy rule-based modeling approach for financial time series forecasting. IEEE International Conference on Fuzzy Systems. https://doi.org/10.1109/FUZZ-IEEE.2017.8015654
Majidpour, M., Nazaripouya, H., Chu, P., Pota, H., & Gadh, R. (2018). Fast Univariate Time Series Prediction of Solar Power for Real-Time Control of Energy Storage System. Forecasting, 1(1), 107–120. https://doi.org/10.3390/forecast1010008
Mapuwei, T. W., Bodhlyera, O., & Mwambi, H. (2020). Univariate Time Series Analysis of Short-Term Forecasting Horizons Using Artificial Neural Networks: The Case of Public Ambulance Emergency Preparedness. Journal of Applied Mathematics, 2020. https://doi.org/10.1155/2020/2408698
Mohammed, J., Bahadoorsingh, S., Ramsamooj, N., & Sharma, C. (2017). Performance of exponential smoothing, a neural network and a hybrid algorithm to the short term load forecasting of batch and continuous loads. 2017 IEEE Manchester PowerTech, Powertech 2017. https://doi.org/10.1109/PTC.2017.7980816
Mudiyanselage, K., & Banda, U. (2018). Forecasting Ability of Univariate Time Series Approach in Foreign Guest Nights in the Southern Coast of Sri Lanka, 5(August), 17–25.
Muhamad, N. S., & Din, A. M. (2016). Neural Network Forecasting Model using Smoothed Data. In The 4th International Conference on Quantitative Sciences and Its Applications (ICOQSIA 2016) (Vol. 4). https://doi.org/10.1063/1.4966079
Nazaripouya, H., Wang, B., Wang, Y., Chu, P., Pota, H. R., & Gadh, R. (2016). Univariate time series prediction of solar power using a hybrid wavelet-ARMA-NARX prediction method. Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, 2016-July, 1–5. https://doi.org/10.1109/TDC.2016.7519959
Peng, J., Kim, M., Jo, M., Min, D., Kim, K., Lee, B., & Kim, B. (2017). Accuracy Evaluation of the Crop-Weather Yield Predictive Models of Italian Ryegrass and Forage Rye Using Cross-Validation, 2017(10), 327–334.
Penpece, D., & Elma, O. E. (2014). Predicting Sales Revenue by Using Artificial Neural Network in Grocery Retailing Industry: A Case Study in Turkey. International Journal of Trade, Economics and Finance, 5(5), 435–440. https://doi.org/10.7763/ijtef.2014.v5.411
Phumchusri, N., & Ungtrakul, P. (2020). Hotel daily demand forecasting for high-frequency and complex seasonality data: a case study in Thailand. Journal of Revenue and Pricing Management, 19(1), 8–25. https://doi.org/10.1057/s41272-019-00221-6
Putra, H., & Ulfa Walmi, N. (2020). Penerapan Prediksi Produksi Padi Menggunakan Artificial Neural Network Algoritma Backpropagation. Jurnal Nasional Teknologi Dan Sistem Informasi, 6(2), 100–107. https://doi.org/10.25077/teknosi.v6i2.2020.100-107
Ravinder, H. V. (2013). Forecasting With Exponential Smoothing Whats The Right Smoothing Constant? Review of Business Information Systems (RBIS), 17(3), 117–126. https://doi.org/10.19030/rbis.v17i3.8001
Sethi, J. K., & Mittal, M. (2020). Analysis of air quality using univariate and multivariate time series models. Proceedings of the Confluence 2020 - 10th International Conference on Cloud Computing, Data Science and Engineering, 823–827. https://doi.org/10.1109/Confluence47617.2020.9058303
Smyl, S. (2020). A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting, 36(1), 75–85. https://doi.org/10.1016/j.ijforecast.2019.03.017
Sulandari, W., Subanar, Suhartono, & Utami, H. (2016). Forecasting electricity load demand using hybrid exponential smoothing-artificial neural network model. International Journal of Advances in Intelligent Informatics, 2(3), 131–139. https://doi.org/10.26555/ijain.v2i3.69
Syamsiah, N. O. (2020). PERAMALAN HARGA TELUR AYAM RAS DI JAKARTA TIMUR. Journal of Computer Engineering, System and Science, 5(1), 65–69.
Syamsiah, N. O., & Purwandani, I. (2019). Penerapan Neural Network Untuk Peramalan Data Time Series Univariate Jumlah Wisatawan Mancanegara. Jurnal Mantik Penusa, 3(3), 100–106. Retrieved from http://e-jurnal.pelitanusantara.ac.id/index.php/mantik/article/view/675
Wang, C., Wang, G., Zhang, X., & Zhang, S. (2015). Direct Forecast Method Based on ANN in Network Traffic Prediction. In W. E. Wong (Ed.), International Conference on Computer Engineering and Networks (pp. 477–484). Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-319-11104-9
Xiao, C., Xia, W., & Jiang, J. (2020). Stock price forecast based on combined model of ARI-MA-LS-SVM. Neural Computing and Applications, 5. https://doi.org/10.1007/s00521-019-04698-5
Zhang, Y., Zhong, M., Geng, N., & Jiang, Y. (2017). Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China. PLoS ONE, 12(5), 1–15. https://doi.org/10.1371/journal.pone.0176729
Zhao, J., Wang, Z., Zhang, Z., & Han, Y. (2019). A combined model based on GM and SARIMA: An example of excavator demand forecasting. 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019, 232–236. https://doi.org/10.1109/ICCCBDA.2019.8725752
Abstract viewed = 147 times
PDF downloaded = 169 times
Copyright (c) 2020 Nurfia Oktaviani Syamsiah, Indah Purwandani
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Jurnal Riset informatika uses the rule of law to access digital electronic articles under the Creative Commons Attribution-NonCommercial 4.0 International License, which means that all content is available free of charge to users or their institutions. You can remix, tweak, and work on non-commercial works, and although new works must also acknowledge the creators and be non-commercial, you don't need to license derivative works under the same terms.