A new hybrid approach EMD-EXP for short-term forecasting of daily stock market time series data


Abstract


Forecasting time series recently has attracted considerable attention in the field of analyzing financial time series data specifically stock market index.  This considerable attention confined itself in the need of transparent change in the governmental policies whether attracting foreign investment or/and economical advancements. In this study, a hybrid methodology between Empirical Mode Decomposition with exponential smoothing method (EMD-EXP) is used to improve forecasting performances in financial time series. The strength of this EMD-EXP lies in its ability to predict non-stationary and non-linear time series without need to use any transformation method. Moreover, EMD-EXP also has relatively high accuracy and offer a new forecasting method in time series. The daily stock market time series data of 12 countries are applied to show the forecasting performance of the proposed EMD-EXP. Based on the three forecast accuracy measures, the results indicate that EMD-EXP forecasting performance is superior to seven traditional forecasting methods.

Keywords: forecast time series; empirical mode decomposition (EMD);exponential smoothing forecasting(EXP);intrinsic mode function (IMF); seasonal-trend decomposition (STL).

References


Abadan, S. and Shabri, A. (2014). Hybrid empirical mode decomposition-arima for forecasting price of rice. Applied Mathematical Sciences, 8(63):3133–3143.

Andrew, H. C. (1989). Forecasting, structural time series models and the kalman filter.

Cambridge University.

Assimakopoulos, V. and Nikolopoulos, K. (2000). The theta model: a decomposition approach to forecasting. International journal of forecasting, 16(4):521–530.

Bergmeir, C., Hyndman, R. J., and Ben´ıtez, J. M. (2016). Bagging exponential smoothing methods using stl decomposition and box–cox transformation. International Journal of Forecasting, 32(2):303–312.

Chen, C.-F., Lai, M.-C., and Yeh, C.-C. (2012). Forecasting tourism demand based on empirical mode decomposition and neural network. Knowledge-Based Systems, 26:281–287.

Cheng, C.-H. and Wei, L.-Y. (2014). A novel time-series model based on empirical mode decomposition for forecasting taiex. Economic Modelling, 36:136–141.

Cleveland, R. B., Cleveland, W. S., McRae, J. E., and Terpenning, I. (1990). Stl: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 6(1):3–73.

Cleveland, W. S., Devlin, S. J., and Terpenning, I. J. (1981). The details of the sabl transformation, decomposition and calendar methods. Bell Laboratories, Murray Hill, NJ.

Cleveland, W. S., Grosse, E., and Shyu, W. M. (1992). Local regression models. Statistical

models in S, pages 309–376.

Coughlin, K. and Tung, K.-K. (2004). 11-year solar cycle in the stratosphere extracted by the empirical mode decomposition method. Advances in space research, 34(2):323– 329.

Dagum, E. B. (1988). The x11arima/88 seasonal adjustment method-foundations and users manual, time series research and analysis division. Statistics Canada technical report

Deering, R. and Kaiser, J. F. (2005). The use of a masking signal to improve empirical mode decomposition. In Acoustics, Speech, and Signal Processing, 2005. Proceedings.( ICASSP’05). IEEE International Conference on, volume 4, pages iv–485. IEEE.

Gardner, E. S. (1985). Exponential smoothing: The state of the art. Journal of forecasting, 4(1):1–28.

Guo, Z., Zhao, W., Lu, H., and Wang, J. (2012). Multi-step forecasting for wind speed using a modified emd-based artificial neural network model. Renewable Energy, 37(1):241–249.

Harvey, A. C. and Peters, S. (1990). Estimation procedures for structural time series models. Journal of Forecasting, 9(2):89–108.

Holt, C. (1957). Forecasting trends and seasonals by exponentially weighted averages onr.

Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., and Liu, H. H. (1998). The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, volume 454, pages 903–995. The Royal Society.

Huang, W., Sher, Y.-P., Peck, K., and Fung, Y. C. B. (2002). Matching gene activity with physiological functions. Proceedings of the National Academy of Sciences, 99(5):2603– 2608.

Hyndman, R., Koehler, A. B., Ord, J. K., and Snyder, R. D. (2008). Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media.

Hyndman, R. J., Akram, M., Archibald, B. C., and Archibald, B. (2003). The admissible parameter space for exponential smoothing models.

Hyndman, R. J. and Billah, B. (2003). Unmasking the theta method. International Journal of Forecasting, 19(2):287–290.

Hyndman, R. J., Koehler, A. B., Snyder, R. D., and Grose, S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18(3):439–454.

Li, R. and Wang, Y. (2008). Short-term wind speed forecasting for wind farm based on empirical mode decomposition. In Electrical Machines and Systems, 2008. ICEMS 2008. International Conference on, pages 2521–2525. IEEE.

Lin, C.-S., Chiu, S.-H., and Lin, T.-Y. (2012). Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting. Economic Modelling, 29(6):2583–2590.

Lu, Y., Oruklu, E., and Saniie, J. (2013). Chirplet signal and empirical mode decompositions of ultrasonic signals for echo detection and estimation. Journal of Signal and Information Processing, 4(02):149.

Makridakis, S. and Hibon, M. (2000). The m3-competition: results, conclusions and implications. International journal of forecasting, 16(4):451–476.

Muck, J. and Skrzypczynski, P. (2012). Can we beat the random walk in forecasting cee exchange rates? Available at SSRN 2163518.

Oh, H.-S., Suh, J.-H., and Kim, D.-H. (2009). A multi-resolution approach to nonstationary financial time series using the hilbert-huang transform. Korean Journal of Applied Statistics, 22(3):499–513.

Pegels, C. C. (1969). Exponential forecasting: Some new variations. Management Science, pages 311–315.

Peiris, M. and Perera, B. (1988). On prediction with fractionally differenced arima models. Journal of Time Series Analysis, 9(3):215–220.

Persons, W. M. (1919). Indices of General Business Condoitions. Harvard Univ. Committee on Economic research.

Rilling, G., Flandrin, P., Goncalves, P., et al. (2003). On empirical mode decomposition and its algorithms. In IEEE-EURASIP workshop on nonlinear signal and image processing, volume 3, pages 8–11. NSIP-03, Grado (I).

Rossi, M. and Brunelli, D. (2015). Forecasting data centers power consumption with the holt-winters method. In Environmental, Energy and Structural Monitoring Systems (EESMS), 2015 IEEE Workshop on, pages 210–214. IEEE.

Rowland, P. et al. (2003). Forecasting the USD/COP Exchange Rate: A Random Walk with a Variable Drift. Banco de la Rep´ublica.

Tatinati, S. and Veluvolu, K. C. (2013). A hybrid approach for short-term forecasting of wind speed. The Scientific World Journal, 2013.

Theodosiou, M. (2011). Forecasting monthly and quarterly time series using stl decomposition. International Journal of Forecasting, 27(4):1178–1195.

Turner, L. W. and Witt, S. F. (2001). Forecasting tourism using univariate and multivariate structural time series models. Tourism Economics, 7(2):135–147.

Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3):324–342.

Yang, D., Sharma, V., Ye, Z., Lim, L. I., Zhao, L., and Aryaputera, A. W. (2015). Forecasting of global horizontal irradiance by exponential smoothing, using decompositions. Energy, 81:111–119.

Yu, L., Wang, S., and Lai, K. K. (2008). Forecasting crude oil price with an emd-basedneural network ensemble learning paradigm. Energy Economics, 30(5):2623–2635.

Zeng, K. and He, M.-X. (2004). A simple boundary process technique for empirical mode decomposition. In Geoscience and Remote Sensing Symposium, 2004. IGARSS’04. Proceedings. 2004 IEEE International, volume 6, pages 4258–4261. IEEE.

Zhang, R. R., Ma, S., Safak, E., and Hartzell, S. (2003). Hilbert-huang transform analysis of dynamic and earthquake motion recordings. Journal of Engineering Mechanics, 129(8):861–875.


Full Text: pdf
کاغذ a4
Bookmark and Share


Creative Commons License
This work is licensed under a Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia License.