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Recent Advances in Computer Science and Communications

Author(s): Iflah Aijaz and Parul Agarwal*

DOI: 10.2174/1573401315666190619112842

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A Study on Time Series Forecasting using Hybridization of Time Series Models and Neural Networks

Page: [827 - 832] Pages: 6

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Abstract

Introduction: Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) are leading linear and non-linear models in Machine learning respectively for time series forecasting.

Objective: This survey paper presents a review of recent advances in the area of Machine Learning techniques and artificial intelligence used for forecasting different events.

Methods: This paper presents an extensive survey of work done in the field of Machine Learning where hybrid models for are compared to the basic models for forecasting on the basis of error parameters like Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Normalized Root Mean Square Error (NRMSE).

Results: Table 1 summarizes important papers discussed in this paper on the basis of some parameters which explain the efficiency of hybrid models or when the model is used in isolation.

Conclusion: The hybrid model has realized accurate results as compared when the models were used in isolation yet some research papers argue that hybrids cannot always outperform individual models.

Keywords: ARIMA, ANN, time series forecasting, hybrids, MSE, MAPE.

Graphical Abstract

[1]
R. Madan, and P. Sarathimangipudi, "Predicting computer network traffic: A time series forecasting approach using dwt, arima and rnn , 2018 11th International Conference on Contemporary Computing, IC3 2018, 2018, pp. 2-4",
[http://dx.doi.org/10.1109/IC3.2018.8530608]
[2]
Y.U. Shui-Ling, "Stock price prediction based on ARIMA-RNN combined model", 4th International Conference on Social Science (ICSS), 2017 978-1-60595-525-4
[3]
M. Khashei, and M. Bijari, "A novel hybridization of artificial neural networks and ARIMA models for time series forecasting", Appl. Soft Comput., vol. 11, no. 2, pp. 2664-2675, 2011.
[http://dx.doi.org/10.1016/j.asoc.2010.10.015]
[4]
P.G. Zhang, "Time series forecasting using a hybrid ARIMA and neural network model", Neurocomputing, vol. 50, pp. 159-175, 2003.
[http://dx.doi.org/10.1016/S0925-2312(01)00702-0]
[5]
D. Shah, and M. Amjad, "Trading bitcoins and online time series prediction, NIPS 2016 Time Series Workshop, pp. 1-15, 2016",
[6]
B. Al-Hnaity, and M. Abbod, "Predicting financial time series data using hybrid model", Stud. Computat. Intell., vol. 650, pp. 19-41, 2016.
[http://dx.doi.org/10.1007/978-3-319-33386-1_2]
[7]
N.I. Indera, I.M. Yassin, A. Zabidi, and Z.I. Rizman, "Non-linear autoregressive with exogeneous input (narx) bitcoin price prediction model using PSO-optimized parameters and moving average technical indicators", J. Fundam. Appl. Sci., vol. 9, no. 3S, p. 791, 2018.
[http://dx.doi.org/10.4314/jfas.v9i3s.61]
[8]
A. Raghava-Raju, "A Machine Learning Approach to Forecast Bitcoin Prices", Int. J. Comput. Appl., vol. 182, no. 24, pp. 39-46, 2018.
[9]
J. Kihoro, R. Otieno, and C. Wafula, "Seasonal time series forecasting: A comparative study of Arima and ANN models", Afr. J. Sci. Technol., vol. 5, no. 2, pp. 41-49, 2011.
[10]
J. Faraway, and C. Chatfield, "Time series forecasting with neural networks: a comparative study using the air line data", J. R. Stat. Soc. Ser. C Appl. Stat., vol. 47, no. 2, pp. 231-250, 2003.
[11]
P.F. Pai, and C.S. Lin, "A hybrid ARIMA and support vector machines model in stock price forecasting", Omega, vol. 33, no. 6, pp. 497-505, 2005.
[http://dx.doi.org/10.1016/j.omega.2004.07.024]
[12]
C. Deb, F. Zhang, J. Yang, S.E. Lee, and K.W. Shah, "A review on time series forecasting techniques for building energy consumption", Renew. Sustain. Energy Rev., vol. 74, pp. 902-924, 2017.
[http://dx.doi.org/10.1016/j.rser.2017.02.085]
[13]
A. Baziar, and A. Kavousi-fard, "Short term load forecasting using a hybrid model based on support vector regression", Int. J. Sci. Technol. Res., vol. 4, no. 5, pp. 189-195, 2015.
[14]
A. Jain, and A.M. Kumar, "Hybrid neural network models for hydrologic time series forecasting", Appl. Soft Comput., vol. 7, no. 2, pp. 585-592, 2007.
[http://dx.doi.org/10.1016/j.asoc.2006.03.002]
[15]
L. Wang, H. Zou, J. Su, L. Li, and S. Chaudhry, "An ARIMA-ANN hybrid model for time series forecasting", Syst. Res. Behav. Sci., vol. 30, no. 3, pp. 244-259, 2013.
[http://dx.doi.org/10.1002/sres.2179]
[16]
B. Zhu, and Y. Wei, "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology", Omega (United Kingdom), vol. 41, no. 3, pp. 517-524, 2012.
[17]
T. Taskaya-Temizel, and M.C. Casey, "A comparative study of autoregressive neural network hybrids", Neural Netw., vol. 18, no. 5-6, pp. 781-789, 2005.
[http://dx.doi.org/10.1016/j.neunet.2005.06.003] [PMID: 16085389]
[18]
B. Wang, X. Zhu, Q. He, and G. Gu, "The forecast on the customers of the member point platform built on the blockchain technology by ARIMA and LSTM, 3rd IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2018, 2018pp. 589-59",
[http://dx.doi.org/10.1109/ICCCBDA.2018.8386584]
[19]
Q. Yao, and P.J. Brockwell, "Gaussian maximum likelihood estimation for ARMA models", Int. Time Series. J. Time Series Analysis., vol. 27, no. 6, pp. 857-875, 2006.
[http://dx.doi.org/10.1111/j.1467-9892.2006.00492.x]
[20]
I.M. Chakravarti, "Time series analysis forecasting and control. by George E. P.; Gwilym M. Jenkins", J. Am. Stat. Assoc., vol. 68, no. 342, pp. 493-494, 1973.
[http://dx.doi.org/10.2307/2284112]
[21]
A. Jabbari, and D.H. Bae, "Application of Artificial Neural Networks for accuracy enhancements of real-time flood forecasting in the Imjin basin", Water (Switzerland), vol. 10, p. 11, 2018.
[http://dx.doi.org/10.3390/w10111626]
[22]
S.M. Idrees, M.A. Alam, and P. Agarwal, "A prediction approach for stock market volatility based on time series data", IEEE Access, vol. 7, no. c, pp. 17287-17298, 2019.
[http://dx.doi.org/10.1109/ACCESS.2019.2895252]
[23]
C.H. Fajardo-Toro, J. Mula, and R. Poler, Engineering digital transformation., Springer International Publishing: Berlin, Heidelberg, 2019.
[24]
B.E. Flores, "A pragmatic view of accuracy measurement in forecasting", Omega, vol. 14, no. 2, pp. 93-98, 1986.
[http://dx.doi.org/10.1016/0305-0483(86)90013-7]