Abstract
Time series data consists of a variety of information in its patterns. It is composed of both linear and nonlinear parts. Depending on nature of time series data, either linear model or nonlinear model can be applied. Instead of applying linear time series model like Auto Regressive Integrated Moving Average (ARIMA) and nonlinear time series model like Support Vector Machine (SVM) and Artificial Neural Network (ANN) individually on time series data, the proposed hybrid model decomposes time series data into two parts using Moving Average Filter and applies ARIMA on the linear part of time series data and SVM on nonlinear part of time series data. The performance of the proposed hybrid model is compared using Mean Absolute Error (MAE) and Mean Squared Error (MSE) with the performances obtained by the conventional models like ARIMA, SVM, and ANN individually. The proposed model has shown efficient prediction results when compared with the results given by the conventional models of time series data having trended patterns.
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Suresh, K.K., Priya, S.: Forecasting sugarcane yield of Tamilnadu using ARIMA models. Sugar Tech. 13(1), 23–26 (2011)
Wang, J.-J., Wang, J.-Z., Zhang, Z.-G., Guo, S.-P.: Stock index forecasting based on a hybrid model. Omega 40(6), 758–766 (2012)
Contreras, J., Espinola, R., Nogales, F., Conejo, A.: ARIMA models to predict next-day electricity prices. IEEE Trans. Power Syst. 18(3), 1014–1020 (2003)
Cadenas, E., Rivera, W.: Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model. Renew. Energy 35(12), 2732–2738 (2010)
Gonzalez-Romera, E., Jaramillo-Moran, M., Carmona-Fernandez, D.: Monthly electric energy demand forecasting based on trend extraction. IEEE Trans. Power Syst. 21(4), 1946–1953 (2006)
Szkuta, B.R., Sanabria, A.L., Dillon, T.S.: Electricity price short-term forecasting using artificial neural networks. IEEE Trans. Power Syst. 14(3), 851–857 (1999)
Kisi, O., Cigizoglu, H.K.: Comparison of different ANN techniques in river flow prediction. Civil Eng. Environ. Syst. 24(3), 211–231 (2007)
Chen, W.-S., Du, Y.-K.: Using neural networks and data mining techniques for the financial distress prediction model. Expert Syst. Appl. 36(22), 4075–4086 (2009)
Xie, W., Yu, L., Xu, S., Wang, S.: A new method for crude oil price forecasting based on support vector machines. Comput. Sci. ICCS 2006, 444–451 (2006)
Wen, J., Wang, X., Li, L., Zheng, Y., Zhou, L., Shao, F.: Short-term wind power forecasting based on lifting wavelet, SVM and error forecasting. In: Unifying Electrical Engineering and Electronics Engineering. Springer, New York, pp. 1037–1045 (2014)
Li, X., Gong, D., Li, L., Sun, C.: Next day load forecasting using SVM. In: International Symposium on Neural Networks, pp. 634–639. Springer, Berlin, Heidelberg (2005)
Narendra, B.C., Eswara Reddy, B.: A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data. Appl. Soft Comput. 23, 27–38 (2014)
Yao, Q., Brockwell, P.J.: Gaussian maximum likelihood estimation for ARMA models. I. Time Series. J. Time Ser. Anal. 27(6), 857–875 (2006)
Zhang, G.P.: Neural networks for time-series forecasting. In: Handbook of Natural Computing, pp. 461–477. Springer, Berlin, Heidelberg (2012)
Hyndman, R.J., George A.: Forecasting: principles and practice. OTexts (2014)
Crude Oil Production and Inflation Consumer Sale. http://www.datamarket.com
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Aradhye, G., Rao, A.C.S., Mastan Mohammed, M.D. (2019). A Novel Hybrid Approach for Time Series Data Forecasting Using Moving Average Filter and ARIMA-SVM. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-13-1498-8_33
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DOI: https://doi.org/10.1007/978-981-13-1498-8_33
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