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Neural Processing Letters

Erschienen in:

04.05.2017

Foreign Exchange Rates Forecasting with Convolutional Neural Network

verfasst von: Chen Liu, Weiyan Hou, Deyin Liu

Erschienen in: Neural Processing Letters | Ausgabe 3/2017

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Abstract

In this paper, we introduce a model based on Convolutional Neural Network for forecasting foreign exchange rates. Additionally, a method of transforming exchange rates data from 1D structure to 2D structure is proposed. The transaction of the foreign exchange market has periodic characteristics, however, due to the technical limitations, these characteristics cannot be utilized by existing time series forecasting models. In this paper, we propose a model which can process 2D structure exchange rates data and put these characteristics to good use. Exchange rates Euro against US dollar, US dollar against Japanese yen and British Pound Sterling against US dollar are researched in this paper. Our experimental results show that, when compared with Artificial Neural Network, Support Vector Regression and Gated Recurrent Unit, the proposed model can effectively improve the accuracy of long-term forecasting.

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Zhang Y, Zhao D, Sun J, Zou G, Li W (2016) Adaptive convolutional neural network and its application in face recognition. Neural Process Lett 43(2):389–399. doi:10.1007/s11063-015-9420-y CrossRef

Abdel-Hamid O, Mohamed A, Jiang H, Penn G (2012) Applying convolutional neural networks concepts to hybrid nn-hmm model for speech recognition. In: 2012 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 4277–4280. doi:10.1109/ICASSP.2012.6288864

Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651. doi:10.1109/TPAMI.2016.2572683 CrossRef

Gao ZK, Cai Q, Yang YX, Dang WD, Zhang SS (2016) Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series. Sci Rep 6:35622. doi:10.1038/srep35622 CrossRef

Gao ZK, Jin ND (2012) A directed weighted complex network for characterizing chaotic dynamics from time series. Nonlinear Anal Real World Appl 13(2):947–952. doi:10.1016/j.nonrwa.2011.08.029 MathSciNetCrossRefMATH

Gao ZK, Cai Q, Yang YX, Dong N, Zhang SS (2016) Visibility graph from adaptive optimal kernel time-frequency representation for classification of epileptiform eeg. Int J Neural Syst 27(4):1750005. doi:10.1142/S0129065717500058 CrossRef

Gao ZK, Fang PC, Ding MS, Jin ND (2015a) Multivariate weighted complex network analysis for characterizing nonlinear dynamic behavior in two-phase flow. Exp Therm Fluid Sci 60:157–164. doi:10.1016/j.expthermflusci.2014.09.008 CrossRef

Gao ZK, Yang YX, Fang PC, Zou Y, Xia CY, Du M (2015) Multiscale complex network for analyzing experimental multivariate time series. Eur Lett 109:30005. http://stacks.iop.org/0295-5075/109/i=3/a=30005

Hu W, Yan L, Liu K, Wang H (2016) A short-term traffic flow forecasting method based on the hybrid pso-svr. Neural Process Lett 43(1):155–172. doi:10.1007/s11063-015-9409-6 CrossRef

10.

Hu W, Yan L, Wang H, Du B, Tao D (2017) Real-time traffic jams prediction inspired by biham, middleton and levine (bml) model. Inf Sci 381:209–228. doi:10.1016/j.ins.2016.11.023 CrossRef

11.

Galeshchuk S (2016) Neural networks performance in exchange rate prediction. Neurocomputing 172:446–452. doi:10.1016/j.neucom.2015.03.100 CrossRef

12.

Sermpinis G, Stasinakis C, Theofilatos K, Karathanasopoulos A (2015) Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms support vector regression forecast combinations. Eur J Op Res 247(3):831–846. doi:10.1016/j.ejor.2015.06.052 MathSciNetCrossRefMATH

13.

Box GEP, Jenkins GM, Reinsel GC (2008) Linear stationary models. pp 47–91. doi:10.1002/9781118619193.ch3

14.

Box GEP, Jenkins GM, Reinsel GC (2008) Linear nonstationary models. pp 93–136. doi:10.1002/9781118619193.ch4

15.

Ferreira TAE, Vasconcelos GC, Adeodato PJL (2008) A new intelligent system methodology for time series forecasting with artificial neural networks. Neural Process Lett 28(2):113–129. doi:10.1007/s11063-008-9085-x CrossRef

16.

Elman JL (1990) Finding structure in time. Cogn Sci 14(2):179–211. doi:10.1016/0364-0213(90)90002-E CrossRef

17.

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. doi:10.1162/neco.1997.9.8.1735 CrossRef

18.

Cho K, van Merrienboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: encoder-decoder approaches. CoRR. http://arxiv.org/abs/1409.1259

19.

Bech ML, Sobrun J (2013) Fx market trends before, between and beyond triennial surveys. BIS Q Rev. http://www.bis.org/publ/qtrpdf/r_qt1312f.htm

20.

Deng S, Yoshiyama K, Mitsubuchi T, Sakurai A (2015) Hybrid method of multiple kernel learning and genetic algorithm for forecasting short-term foreign exchange rates. Comput Econ 45(1):49–89. doi:10.1007/s10614-013-9407-6 CrossRef

21.

Chiarella C, Peat M, Stevenson M (1994) Detecting and modelling nonlinearity in flexible exchange rate time series. Asia Pac J Manag 11(2):159–186. doi:10.1007/BF01739197 CrossRef

22.

Brooks C (1996) Testing for non-linearity in daily sterling exchange rates. Appl Financ Econ 6(4):307–317. doi:10.1080/096031096334105 CrossRef

23.

Abdel-Hamid O, Li D, Dong Y (2013) Exploring convolutional neural network structures and optimization techniques for speech recognition. In: Interspeech 2013

24.

Samek D, Varacha P (2013) Time series prediction using artificial neural networks: single and multi-dimensional data. Int J Math Models Methods Appl Sci 7(1):38–46

25.

Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J physiol 160(1):106–154CrossRef

26.

Fukushima K (1988) A neural network for visual pattern recognition. Computer 21(3):65–75. doi:10.1109/2.32 CrossRef

27.

LeCun Y, Bengio Y (1998) Convolutional networks for images, speech, and time series. In: Arbib MA (ed) The handbook of brain theory and neural networks. MIT Press, Cambridge, MA, pp 255–258

28.

Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. doi:10.1109/5.726791 CrossRef

29.

Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159MathSciNetMATH

30.

Dean J, Corrado GS, Monga R, Chen K, Devin M, Le QV, Mao MZ, Ranzato M, Senior A, Tucker P, Yang K, Ng AY (2012) Large scale distributed deep networks. In: Proceedings of the 25th international conference on neural information processing systems, MIT Press, Cambridge, MA, USA, pp 1223–1231

31.

Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958MathSciNetMATH

32.

Wang L, Qiao Y, Tang X (2015) Action recognition with trajectory-pooled deep-convolutional descriptors. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 4305–4314. doi:10.1109/CVPR.2015.7299059

33.

Ji Y, Hao J, Reyhani N, Lendasse A (2005) Direct and recursive prediction of time series using mutual information selection, pp 1010–1017. doi:10.1007/11494669_124

34.

Page ES (1954) Continuous inspection schemes. Biometrika 41:100–115. doi:10.1093/biomet/41.1-2.100 MathSciNetCrossRefMATH

35.

Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mané D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Viégas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2015) TensorFlow: large-scale machine learning on heterogeneous systems. http://tensorflow.org/, software available from tensorflow.org

Titel: Foreign Exchange Rates Forecasting with Convolutional Neural Network
verfasst von: Chen Liu
Weiyan Hou
Deyin Liu
Publikationsdatum: 04.05.2017
Verlag: Springer US
Erschienen in: Neural Processing Letters / Ausgabe 3/2017
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-017-9629-z

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