Forecasting Financial Time Series using a Hybrid Non-stationary Model with ANN
DOI:
https://doi.org/10.26438/ijcse/v7i1.323326Keywords:
ARIMA-GARCH, Trend, Hybrid and Accuracy, ARIMA-ANNAbstract
Forecasting financial time series have been regarded as one of the most challenging applications of modern time series forecasting. Thus, numerous models have been depicted to provide the investors with more precise predictions. In recent years, financial market dynamics forecasting has been a focus of economic research. In this paper, we propose a hybrid nonstationary time series model with artificial neural network (ANN) for forecasting financial time series. The proposed model is non-stationary in trend component with regressor, lagged variable and non-linear component. The proposed model can capture both linear and non-linear structures in the time series. Non-linear structure is capture by Fed-Forward Neural Networks (FNN). The working of the proposed model is examined for SPY and VOO stock prices. Forecast based on the proposed model performs better than existing models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percent Error (MAPE) criterion.
References
[1] C.H.Aladag, E.Egrioglu, C.Kadil, “Forecasting nonlinear time series with a hybrid methodology”, Applied Mathematics Letters,Vol.22, Issue.9, pp.1467-1470, 2009.
[2] F.M. Tseng, H.C. Yu, G.H. Tzeng, “Combining neural network model with seasonal time series ARIMA model”, Technological Forecasting & Social Change,Vol. 69, Issue.1 , pp.71-87, 2002.
[3] G. Zhang, E.B. Patuwo, M.Y. Hu, “Forecasting with artificial neural networks: the state of the art”, Journal of Forecasting,Vol. 1, Issue.1, pp.35-62, 1998.
[4] G.E.P. Box, G.M. Jenkins, “Time Series Analysis: Forecasting and Control”, Holden-Day, San Francisco, 1970.
[5] G.Zhang, “Time series forecasting using a hybrid ARIMA and neural network model”,Neurocomputing,Vol. 50 , Issue. 17, pp.159-175, 2003.
[6] M.H. Ahmad, P.Y. Ping, S.R Yaziz, N.A Miswan, “A Hybrid Model for Improving Malaysian Gold Forecast Accuracy”, International Journal of Mathematical Analysis, Vol. 8, Issue. 28, pp. 1377-1387, 2014.
[7] P.F. Pai, C.S. Lin, “A hybrid ARIMA and support vector machines model in stock price forecasting”, The International journal of Management Science, Vol. 33, Issue. 6, pp. 497-505, 2005.
[8] R.S. Tsay, “Analysis of Financial Time Series”, 2nd ed., John Wiley& Sons, U.K, 2005.
[9] T.Nakatsuma, “Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach”, Journal of Econometrics, Vol. 95, Issue. 1, pp. 57-69 2000.
[10] W.W.S Wei, “Time Series Analysis: Univariate and Multivariate methods”, Pearson, San Francisco, 2006.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
