A hybrid model for financial time-series forecasting based on mixed methodologies
Zhidan Luo
School of Statistics, University of International Business and Economics, Beijing, China
Search for more papers by this authorWei Guo
School of Statistics, University of International Business and Economics, Beijing, China
Search for more papers by this authorCorresponding Author
Qingfu Liu
Institute for Financial Studies, Fudan University, Shanghai, China
Correspondence
Qingfu Liu, Institute for Financial Studies, Fudan University, Shanghai, China.
Email: [email protected]
Search for more papers by this authorZhengjun Zhang
Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, USA
Search for more papers by this authorZhidan Luo
School of Statistics, University of International Business and Economics, Beijing, China
Search for more papers by this authorWei Guo
School of Statistics, University of International Business and Economics, Beijing, China
Search for more papers by this authorCorresponding Author
Qingfu Liu
Institute for Financial Studies, Fudan University, Shanghai, China
Correspondence
Qingfu Liu, Institute for Financial Studies, Fudan University, Shanghai, China.
Email: [email protected]
Search for more papers by this authorZhengjun Zhang
Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, USA
Search for more papers by this authorFunding information: National Natural Science Foundation of China, Grant/Award Numbers: 61973084, 71871066, 71991471; NSF-DMS, Grant/Award Number: 2012298; the Shanghai Science and Technology Innovation Action Plan Project, Grant/Award Number: 19511101700
Abstract
This paper proposes a hybrid model that combines ensemble empirical mode decomposition (EEMD), autoregressive integrated moving average (ARIMA), and Taylor expansion using a tracking differentiator to forecast financial time series. Specifically, the financial time series is decomposed by EEMD into some subseries. Then, the linear portion of each subseries is forecasted by the linear ARIMA model, while the nonlinear portion is predicted by the nonlinear Taylor expansion model. The forecasting results of the linear and nonlinear models are combined into the predicted result of each subseries. The final prediction result is obtained by combining the prediction values of all the subseries. The empirical results with real financial time series data demonstrate that this new hybrid approach outperforms the benchmark hybrid models considered in this paper.
CONFLICT OF INTEREST
The authors declare that this paper does not have any conflicts of interest.
REFERENCES
- Ahmad, M. A., & Mohd, T. I. (2017). A hybrid approach EMD-MA for short-term forecasting of daily stock market time series data. Journal of Internet Banking and Commerce, 22(1), 1–10. https://doi.org/10.1063/1.4995933
- Akman, E., Karaman, A. S., & Kuzey, C. (2020). Visa trial of international trade: Evidence from support vector machines and neural networks. Journal of Management Analytics, 7, 1–22. https://doi.org/10.1080/23270012.2020.1731719
- Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
- Box, G., & Jenkins, G. (1970). Time series analysis: Forecasting and control. San Francisco, CA: Holden-Day.
- Crone, S. F., Hibon, M., & Nikolopoulos, K. (2011). Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction. International Journal of Forecasting, 27(3), 635–660. https://doi.org/10.1016/j.ijforecast.2011.04.001
- Guo, B., Han, J., & Xi, F. (2002). Linear tracking-differentiator and application to online estimation of the frequency of a sinusoidal signal with random noise perturbation. International Journal of Systems Science, 33(5), 351–358. https://doi.org/10.1080/00207720210121771
- Guo, B., & Zhao, Z. (2011). On convergence of tracking differentiator. International Journal of Control, 84(4), 693–701. https://doi.org/10.1080/00207179.2011.569954
- Han, J., & Wang, W. (1994). Nonlinear tracking-differentiator. Journal of Systems Science and Mathematical Sciences, 14(2), 177–183 in Chinese.
- Huang, N., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., … Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995. https://doi.org/10.1098/rspa.1998.0193
- Huang, S. C., & Wu, T. K. (2008). Combining wavelet-based feature extractions with relevance vector machines for stock index forecasting. Expert Systems, 25(2), 133–149. https://doi.org/10.1111/j.1468-0394.2008.00443.x
- Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1), 307–319. https://doi.org/10.1016/S0925-2312(03)00372-2
- Kock, A. B., & Teräsvirta, T. (2014). Forecasting performances of three automated modelling techniques during the economic crisis 2007-2009. International Journal of Forecasting, 30(3), 616–631. https://doi.org/10.1016/j.ijforecast.2013.01.003
- Kožić, I., & Sever, I. (2014). Measuring business cycles: Empirical mode de-composition of economic time series. Economics Letters, 123(3), 287–290. https://doi.org/10.1016/j.econlet.2014.03.009
- Liu, F., Wang, X., & Zhang, M. (2006). An application of nonlinear trace differential to simulating and forecasting in stock price. Systems Engineering, 24(5), 81–87 in Chinese.
- Lu, Y. (2019). Artificial intelligence: A survey on evolution, models, applications and future trends. Journal of Management Analytics, 6, 1–29. https://doi.org/10.1080/23270012.2019.1570365
- Pai, P., & Lin, C. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33(6), 497–505. https://doi.org/10.1016/j.omega.2004.07.024
- Plakandaras, V., Gupta, R., Gogas, P., & Papadimitriou, T. (2015). Forecasting the U. S. Real House Price Index. Economic Modelling, 45, 259–267. https://doi.org/10.2139/ss-rn.2431627
- Shi, S., Xu, L., & Liu, B. (1996). Applications of artificial neural networks to the nonlinear combination of forecasts. Expert Systems, 13(3), 195–201. https://doi.org/10.1111/j.1468-0394.1996.tb00119.x
- Shi, S., Xu, L., & Liu, B. (1999). Improving the accuracy of nonlinear combined forecasting using neural networks. Expert Systems with Applications, 16, 49–54. https://doi.org/10.1016/s0957-4174(98)00030-x
- Tiwari, A. K., Dar, A. B., Bhanja, N., & Gupta, R. (2016). A historical analysis of the US stock price index using empirical mode decomposition over 1791-2015. Economics, 10(9), 1–15. https://doi.org/10.5018/economics-ejournal.ja.2016-9
- Vafeiadis, T., Dimitriou, N., Ioannidis, D., Wotherspoon, T., Tinker, G., & Tzovaras, D. (2018). A framework for inspection of dies attachment on PCB utilizing machine learning techniques. Journal of Management Analytics, 5(2), 81–94. https://doi.org/10.1080/23270012.2018.1434425
- Wang, D., Wang, Y., Song, X., & Liu, Y. (2018). Coal overcapacity in China: Multiscale analysis and prediction. Energy Economics, 70, 244–257. https://doi.org/10.1016/j.eneco.2018.01.004
- Wang, J., Wang, J., Zhang, Z., & Guo, S. (2012). Stock index forecasting based on a hybrid model. Omega, 40(6), 758–766. https://doi.org/10.1016/j.omega.2011.07.008
- Wang, S., Hu, A., Wu, Z., Liu, Y., & Bai, X. (2014). Multiscale combined model based on run-length-judgment method and its application in oil price forecasting. Mathematical Problems in Engineering, 2014, 1–9. https://doi.org/10.1155/2014/513201
- Wegener, C., Spreckelsen, C., Basse, T., & Mettenheim, H. (2016). Forecasting government bond yields with neural networks considering cointegration. Journal of Forecasting, 35(1), 86–92. https://doi.org/10.1002/for.2385
- Wu, Z., & Huang, N. (2009). Ensemble empirical mode decomposition: A noise assisted data analysis method. Advances in Adaptive Data Analysis, 1(1), 1–41. https://doi.org/10.1142/S1793536909000047
10.1142/S1793536909000047 Google Scholar
- Xie, C., Mao, Z., & Wang, G. J. (2015). Forecasting RMB exchange rate based on a nonlinear combination model of ARFIMA, SVM, and BPNN. Mathematical Problems in Engineering, 2015(4), 1–10. https://doi.org/10.1155/2015/635345
- Yang, H., & Lin, H. (2016). An integrated model combined ARIMA, EMD with SVR for stock indices forecasting. International Journal on Artificial Intelligence Tools, 25(2), 1–22. https://doi.org/10.1142/S0218213016500056
- Yang, H., & Lin, H. (2017). Applying the hybrid model of EMD, PSR, and ELM to exchange rates forecasting. Computational Economics, 49(1), 99–116. https://doi.org/10.1007/s10614-015-9549-9
- Yuan, R., Li, Z., Guan, X., & Xu, L. (2010). An SVM-based machine learning method for accurate internet traffic classification. Information Systems Frontiers, 12, 149–156. https://doi.org/10.1007/s10796-008-9131-2
- Zeng, Q., Qu, C., Ng, A., & Zhao, X. (2016). A new approach for Baltic dry index forecasting based on empirical mode decomposition and neural networks. Maritime Economics & Logistics, 18(2), 192–210. https://doi.org/10.1057/mel.2015.2
- Zhang, G. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0
- Zhang, G., Zhang, X., & Feng, H. (2016). Forecasting financial time series using a methodology based on autoregressive integrated moving average and Taylor expansion. Expert Systems, 33(5), 501–516. https://doi.org/10.1111/exsy.12164
- Zhang, J., Wei, Y., Tan, Z., Wang, K., & Tian, W. (2017). A hybrid method for short-term wind speed forecasting. Sustainability, 9(4), 1–10. https://doi.org/10.3390/su9040596
- Zhang, N., Lin, A., & Shang, P. (2017). Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting. Physica A, 477, 161–173. https://doi.org/10.1016/j.physa.2017.02.072
- Zhang, X., Lai, K. K., & Wang, S. Y. (2008). A new approach for crude oil price analysis based on empirical mode decomposition. Energy Economics, 30(3), 905–918. https://doi.org/10.1016/j.eneco.2007.02.012
- Zhu, B., Shi, X., Chevallier, J., Wang, P., & Wei, Y. (2016). An adaptive multiscale ensemble learning paradigm for nonstationary and nonlinear energy price time series forecasting. Journal of Forecasting, 35(7), 633–651. https://doi.org/10.1002/for.2395
