Abstract
The neural architecture and the input features are very substantial in order to build an artificial neural network (ANN) model that is able to perform a good prediction. The architecture is determined by several hyperparameters including the number of hidden layers, the number of nodes in each hidden layer, the series length, and the activation function. In this study, we present a method to perform feature selection and architecture selection of ANN model for time series prediction. Specifically, we explore a deep learning or deep neural network (DNN) model, called deep feedforward network, an ANN model with multiple hidden layers. We use two approaches for selecting the inputs, namely PACF based inputs and ARIMA based inputs. Three activation functions used are logistic sigmoid, tanh, and ReLU. The real dataset used is time series data called roll motion of a Floating Production Unit (FPU). Root mean squared error (RMSE) is used as the model selection criteria. The results show that the ARIMA based 3 hidden layers DNN model with ReLU function outperforms with remarkable prediction accuracy among other models.
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References
Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks. Int. J. Forecast. 14, 35–62 (1998)
Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control Sig. Syst. 2, 303–314 (1989)
Funahashi, K.I.: On the approximate realization of continuous mappings by neural networks. Neural Netw. 2, 183–192 (1989)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2, 359–366 (1989)
Chen, Y., He, K., Tso, G.K.F.: Forecasting crude oil prices: a deep learning based model. Proced. Comput. Sci. 122, 300–307 (2017)
Liu, L., Chen, R.C.: A novel passenger flow prediction model using deep learning methods. Transp. Res. Part C: Emerg. Technol. 84, 74–91 (2017)
Qin, M., Li, Z., Du, Z.: Red tide time series forecasting by combining ARIMA and deep belief network. Knowl.-Based Syst. 125, 39–52 (2017)
Qiu, X., Ren, Y., Suganthan, P.N., Amaratunga, G.A.J.: Empirical mode decomposition based ensemble deep learning for load demand time series forecasting. Appl. Soft Comput. 54, 246–255 (2017)
Voyant, C., et al.: Machine learning methods for solar radiation forecasting: a review. Renew. Energy. 105, 569–582 (2017)
Zhao, Y., Li, J., Yu, L.: A deep learning ensemble approach for crude oil price forecasting. Energy Econ. 66, 9–16 (2017)
Hui, L.H., Fong, P.Y.: A numerical study of ship’s rolling motion. In: Proceedings of the 6th IMT-GT Conference on Mathematics, Statistics and its Applications, pp. 843–851 (2010)
Nicolau, V., Palade, V., Aiordachioaie, D., Miholca, C.: Neural network prediction of the roll motion of a ship for intelligent course control. In: Apolloni, B., Howlett, Robert J., Jain, L. (eds.) KES 2007. LNCS (LNAI), vol. 4694, pp. 284–291. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74829-8_35
Zhang, X.L., Ye, J.W.: An experimental study on the prediction of the ship motions using time-series analysis. In: The Nineteenth International Offshore and Polar Engineering Conference (2009)
Khan, A., Bil, C., Marion, K., Crozier, M.: Real time prediction of ship motions and attitudes using advanced prediction techniques. In: Congress of the International Council of the Aeronautical Sciences, pp. 1–10 (2004)
Wang, Y., Chai, S., Khan, F., Nguyen, H.D.: Unscented Kalman Filter trained neural networks based rudder roll stabilization system for ship in waves. Appl. Ocean Res. 68, 26–38 (2017)
Yin, J.C., Zou, Z.J., Xu, F.: On-line prediction of ship roll motion during maneuvering using sequential learning RBF neural networks. Ocean Eng. 61, 139–147 (2013)
Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 50, 159–175 (2003)
Makridakis, S., Wheelwright, S.C., Hyndman, R.J.: Forecasting: Methods and Applications. Wiley, Hoboken (2008)
Wei, W.W.S.: Time Series Analysis: Univariate and Multivariate Methods. Pearson Addison Wesley, Boston (2006)
Tsay, R.S.: Analysis of Financial Time Series. Wiley, Hoboken (2002)
Durbin, J.: The fitting of time-series models. Revue de l’Institut Int. de Statistique/Rev. Int. Stat. Inst. 28, 233 (1960)
Levinson, N.: The wiener (root mean square) error criterion in filter design and prediction. J. Math. Phys. 25, 261–278 (1946)
Liang, F.: Bayesian neural networks for nonlinear time series forecasting. Stat. Comput. 15, 13–29 (2005)
Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2015)
Fausett, L.: Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice-Hall, Inc., Upper Saddle River (1994)
El-Telbany, M.E.: What quantile regression neural networks tell us about prediction of drug activities. In: 2014 10th International Computer Engineering Conference (ICENCO), pp. 76–80. IEEE (2014)
Taylor, J.W.: A quantile regression neural network approach to estimating the conditional density of multiperiod returns. J. Forecast. 19, 299–311 (2000)
Han, J., Moraga, C.: The influence of the sigmoid function parameters on the speed of backpropagation learning. In: Mira, J., Sandoval, F. (eds.) IWANN 1995. LNCS, vol. 930, pp. 195–201. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-59497-3_175
Karlik, B., Olgac, A.V.: Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int. J. Artif. Intell. Expert Syst. 1, 111–122 (2011)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning. pp. 807–814. Omnipress, Haifa (2010)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
LeCun, Y.A., Bottou, L., Orr, G.B., Müller, K.-R.: Efficient BackProp. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 9–48. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35289-8_3
De Gooijer, J.G., Hyndman, R.J.: 25 years of time series forecasting. Int. J. Forecast. 22, 443–473 (2006)
Fuller, W.A.: Introduction to Statistical Time Series. Wiley, Hoboken (2009)
Phillips, P.C.B., Perron, P.: Testing for a Unit Root in Time Series Regression. Biometrika 75, 335 (1988)
Hobijn, B., Franses, P.H., Ooms, M.: Generalizations of the KPSS-test for stationarity. Stat. Neerl. 58, 483–502 (2004)
Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., Shin, Y.: Testing the null hypothesis of stationarity against the alternative of a unit root. J. Econ. 54, 159–178 (1992)
Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Presented at the (2003)
Gensler, A., Henze, J., Sick, B., Raabe, N.: Deep Learning for solar power forecasting — an approach using autoencoder and LSTM neural networks. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 002858–002865. IEEE (2016)
Ryu, S., Noh, J., Kim, H.: Deep neural network based demand side short term load forecasting. In: 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 308–313. IEEE (2016)
Acknowledgements
This research was supported by ITS under the scheme of “Penelitian Pemula” No. 1354/PKS/ITS/2018. The authors thank to the Head of LPPTM ITS for funding and to the referees for the useful suggestions.
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Suhermi, N., Suhartono, Rahayu, S.P., Prastyasari, F.I., Ali, B., Fachruddin, M.I. (2019). Feature and Architecture Selection on Deep Feedforward Network for Roll Motion Time Series Prediction. In: Yap, B., Mohamed, A., Berry, M. (eds) Soft Computing in Data Science. SCDS 2018. Communications in Computer and Information Science, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-3441-2_5
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DOI: https://doi.org/10.1007/978-981-13-3441-2_5
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