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Adaboost-Based Long Short-Term Memory Ensemble Learning Approach for Financial Time Series Forecasting


Affiliations
1 School of Economics and Management, North China Electric Power University, Beijing, 102206, China
 

A hybrid ensemble learning approach is proposed for financial time series forecasting combining AdaBoost algorithm and long short-term memory (LSTM) network. First, LSTM predictor is trained using the training samples obtained by AdaBoost algorithm. Then, AdaBoost algorithm is applied to obtain the ensemble weights of each LSTM predictor. The forecasting results of all the LSTM predictors are combined using ensemble weights to generate our final results. Four major daily exchange rate datasets and two stock market index datasets are selected for model evaluation and model comparison. The empirical study demonstrates that the proposed AdaBoost-LSTM ensemble learning approach outperform other single forecasting models and other ensemble learning approach in terms of both level forecasting accuracy and directional forecasting accuracy. This suggests that the AdaBoost-LSTM ensemble learning approach is a highly promising for financial time rates forecasting.

Keywords

Adaboost Algorithm, Ensemble Learning, Financial Time Series Forecasting, Long Short-Term Memory Network.
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  • Chortareas, G., Jiang, Y. and Nankervis, J. C., Forecasting exchange rate volatility using high-frequency data: is the euro different. Int. J. Forecast., 2011, 27(4), 1089–1107.

  • Tseng, F. et al., Fuzzy ARIMA model for forecasting the foreign exchange market. Fuzzy Sets Syst., 2001, 118(1), 9–19.

  • McCrae, M. et al., Can cointegration-based forecasting outperform univariate models? An application to Asian exchange rates. J. Forecast., 2002, 21(5), 355–380.

  • Moosa, I. A. and Vaz, J. J., Cointegration, error correction and exchange rate forecasting. J. Int. Financ. Mark. Inst. Money, 2016, 44, 21–34.

  • West, K. D. and Cho, D., The predictive ability of several models of exchange rate volatility. J. Econom., 1995, 69(2), 367–391.

  • Carriero, A., Kapetanios, G. and Marcellino, M., Forecasting exchange rates with a large Bayesian VAR. Int. J. Forecast., 2009, 25(2), 400–417.

  • Joseph, N. L., Model specification and forecasting foreign exchange rates with vector autoregressions. J. Forecast., 2001, 20(7), 451–484.

  • Galeshchuk, S., Neural networks performance in exchange rate prediction. Neurocomputing, 2016, 172, 446–452.

  • Kuan, C. M. and Liu, T., Forecasting exchange rates using feed-forward and recurrent neural networks. J. Appl. Econom., 1995, 10(4), 347–364.

  • Zhang, G. and Hu, M. Y., Neural network forecasting of the British pound/US dollar exchange rate. Omega, 1998, 26(4), 495–506.

  • Huang, S. et al., Chaos-based support vector regressions for exchange rate forecasting. Expert Syst. Appl., 2010, 37(12), 8590–8598.

  • Shen, F., Chao, J. and Zhao, J., Forecasting exchange rate using deep belief networks and conjugate gradient method. Neurocomputing, 2015, 167, 243–253.

  • Sun, H. et al., Stacked denoising Autoencoder Based Stock Market Trend Prediction via K-Nearest Neighbour Data Selection. Springer, 2017.

  • Andreou, A. S., Georgopoulos, E. F. and Likothanassis, S. D., Exchange-rates forecasting: a hybrid algorithm based on genetically optimized adaptive neural networks. Comput. Econ., 2002, 20(3), 191–210.

  • Chen, A. and Leung, M. T., Regression neural network for error correction in foreign exchange forecasting and trading. Comput. Oper. Res., 2004, 31(7), 1049–1068.

  • Khashei, M., Bijari, M. and Hejazi, S. R., Combining seasonal ARIMA models with computational intelligence techniques for time series forecasting. Soft Comput., 2012, 16(6), 1091–1105.

  • Nag, A. K. and Mitra, A., Forecasting daily foreign exchange rates using genetically optimized neural networks. J. Forecast., 2002, 21(7), 501–511.

  • Ozorhan, M. O., Toroslu, O. H. and Sehitoglu, O. T., A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms. Soft Comput., 2016, 1–19.

  • Sermpinis, G. et al., Forecasting and trading the EUR/USD exchange rate with stochastic neural network combination and time-varying leverage. Decis. Support Syst., 2012, 54(1), 316– 329.

  • Sermpinis, G. et al., Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and particle swarm optimization. Eur. J. Oper. Res., 2013, 225(3), 528–540.

  • Sermpinis, G. et al., Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms – support vector regression forecast combinations. Eur. J. Oper. Res., 2015, 247(3), 831–846.

  • Singh, U. P. and Jain, S., Optimization of neural network for nonlinear discrete time system using modified quaternion firefly algorithm: case study of Indian currency exchange rate prediction. Soft Comput., 2017, 1–15.

  • Yu, L., Lai, K. K. and Wang, S., Multistage RBF neural network ensemble learning for exchange rates forecasting. Neurocomputing, 2008, 71(16), 3295–3302.

  • Yu, L., Wang, S. and Lai, K. K., A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates. Comput. Oper. Res., 2005, 32(10), 2523–2541.

  • Freund, Y. and Schapire, R. E., A decision-theoretic generalization of on-line learning and an application to boosting: European conference on computational learning theory, Springer, 1995.

  • Hochreiter, S. and Schmidhuber, J., Long short-term memory. Neural Comput., 1997, 9(8), 1735–1780.

  • Liu, H. et al., Comparison of four Adaboost algorithm based artificial neural networks in wind speed predictions. Energy Conv. Manage., 2015, 92, 67–81.


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  • Adaboost-Based Long Short-Term Memory Ensemble Learning Approach for Financial Time Series Forecasting

Abstract Views: 793  |  PDF Views: 124

Authors

Yungao Wu
School of Economics and Management, North China Electric Power University, Beijing, 102206, China
Jianwei Gao
School of Economics and Management, North China Electric Power University, Beijing, 102206, China

Abstract


A hybrid ensemble learning approach is proposed for financial time series forecasting combining AdaBoost algorithm and long short-term memory (LSTM) network. First, LSTM predictor is trained using the training samples obtained by AdaBoost algorithm. Then, AdaBoost algorithm is applied to obtain the ensemble weights of each LSTM predictor. The forecasting results of all the LSTM predictors are combined using ensemble weights to generate our final results. Four major daily exchange rate datasets and two stock market index datasets are selected for model evaluation and model comparison. The empirical study demonstrates that the proposed AdaBoost-LSTM ensemble learning approach outperform other single forecasting models and other ensemble learning approach in terms of both level forecasting accuracy and directional forecasting accuracy. This suggests that the AdaBoost-LSTM ensemble learning approach is a highly promising for financial time rates forecasting.

Keywords


Adaboost Algorithm, Ensemble Learning, Financial Time Series Forecasting, Long Short-Term Memory Network.

References





DOI: https://doi.org/10.18520/cs%2Fv115%2Fi1%2F159-165