Abstract
In this paper, we propose a new multi-scale recursive dynamic factor analysis (MS-RDFA) algorithm for economic index foresting (EIF). The proposed MS-RDFA algorithm first employ empirical mode decomposition (EMD), which is a powerful tool for multi-scale analysis and modeling on the non-linear and non-stationary signal such as economic index data. Moreover, an efficient RDFA algorithm using recursive subspace tracking is adopted to explore the correlated nature of the adjacent intervals of the economic index data. The one-step prediction of PC scores is modeled as an AR process and can be recursively tracked by Kalman filter (KF). The major advantage of the proposed MS-RDFA method is its low arithmetic complexity and simple real-time updating, which is different from other conventional algorithms. This makes it as an attractive alternative to other conventional approaches to EIF on mobile services. The experiments show that the proposed MS-RDFA algorithm has better forecasting results than other EIF methods.
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Jacquier, E., Polson, N.G., Rossi, P.E.: Bayesian analysis of stochastic volatility models with fat-tails and correlated errors. J. Econ. 122(1), 185–212 (2004)
Jobson, J.D., Korkie, B.: Estimation for Markowitz efficient portfolios. J. Amer. Statist. Assoc. 75(371), 544–554 (1980)
Box, G.E.P., Pierce, D.A.: Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J. Amer. Statist. Assoc. 65(332), 1509–1526 (1970)
Islam, M.A., Hassan, M.F., Imam, M.F., Sayem, S.M.: Forecasting coarse rice prices in Bangladesh. Progress Agric. 22, 193–201 (2011)
Nayak, S.C., Misra, B.B., Behera, H.S.: An adaptive second order neural network with genetic-algorithm-based training (ASONN-GA) to forecast the closing prices of the stock market. Int. J. Appl. Metaheuristic Comput. 7(2), 39–57 (2016)
Göçken, M., Özçalici, M., Boru, A., Dosdogru, A.T.: Integrating metaheuristics and artificial neural networks for improved stock price prediction. Expert Syst. Appl. 44, 320–331 (2016)
Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecasting 14(1), 35–62 (1998)
Lu, C.J., Lee, T.S., Chiu, C.C.: Financial time series forecasting using independent component analysis and support vector regression. Decis. Support Syst. 47(2), 115–125 (2009)
Duan, Q., Zhang, L., Wei, F., Xiao, X., Wang, L.: Forecasting model and validation for aquatic product price based on time series GA-SVR. Trans. Chin. Soc. Agric. Eng. 33(1), 308–314 (2017)
He, K., Zha, R., Wu, J., Lai, K.K.: Multivariate EMD-based modeling and forecasting of crude oil price. Sustainability 8(4), 387 (2016)
Shang, H.L.: Nonparametric Modeling and Forecasting Electricity Demand: An Empirical Study, Department of Econometrics and Business Statistics, Monash University, Monash Econometrics and Business Statistics Working Papers, No. 19/10 (2010)
Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Roy. Soc. London A. 454, 903–995 (1998)
Chan, S.C., Wu, H.C., Tsui, K.M.: Robust recursive eigen-decomposition and subspace-based algorithms with application to fault detection in wireless sensor networks. IEEE Trans. Instrum. Meas. 61(6), 1703–1718 (2012)
Liao, B., Zhang, Z.G., Chan, S.C.: A new robust kalman filter based subspace tracking algorithm in an impulsive noise environment. IEEE Trans. Circuits Syst. II, Exp. Briefs 57(9), 740–744 (2010)
Yang, B.: Projection approximation subspace tracking. IEEE Trans. Signal Process. 43(1), 95–107 (1995)
Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn. Johns Hopkins Univ. Press, Baltimore (1996)
Wu, H.C., Chan, S.C., Tsui, K.M., Hou, Y.: A new recursive dynamic factor analysis for point and interval forecast of electricity price. IEEE Trans. Power Syst. 28(3), 2352–2365 (2013)
Fahrmeir, L., Tutz, G.: Multivariate Statistical Modelling Based on Generalized Linear Models. Springer Series in Statistics, 2nd edn. Springer, New York (2001). https://doi.org/10.1007/978-1-4757-3454-6
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This paper was partially supported by Shenzhen Key Laboratory for Intelligent Multimedia and Virtual Reality (ZDSYS201703031405467).
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Yuan, H., Yuan, Y., Wu, H.C., Zou, Y. (2018). Economic Index Forecasting via Multi-scale Recursive Dynamic Factor Analysis. In: Aiello, M., Yang, Y., Zou, Y., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2018. AIMS 2018. Lecture Notes in Computer Science(), vol 10970. Springer, Cham. https://doi.org/10.1007/978-3-319-94361-9_7
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DOI: https://doi.org/10.1007/978-3-319-94361-9_7
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