Abstract
Time-series are widely used for representing non-stationary data such as weather information, health related data, economic and stock market indexes. Many statistical methods and traditional machine learning techniques are commonly used for forecasting time series. With the development of deep learning in artificial intelligence, many researchers have adopted new models from artificial neural networks for forecasting time series. However, poor performance of applying deep learning models in short time series hinders the accuracy in time series forecasting. In this paper, we propose a novel approach to alleviate this problem based on transfer learning. Existing work on transfer learning uses extracted features from a source dataset for prediction task in a target dataset. In this paper, we propose a new training strategy for time-series transfer learning with two source datasets that outperform existing approaches. The effectiveness of our approach is evaluated on financial time series extracted from stock markets. Experiment results show that transfer learning based on 2 data sets is superior than other base-line methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Keras (2015). https://keras.io/
Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: \(12^{th}\) USENIX Symposium on Operating Systems Design and Implementation (OSDI 2016), pp. 265–283 (2016)
Amaral, T., Silva, L.M., Alexandre, L.A., Kandaswamy, C., de Sá, J.M., Santos, J.M.: Transfer learning using rotated image data to improve deep neural network performance. In: Campilho, A., Kamel, M. (eds.) ICIAR 2014. LNCS, vol. 8814, pp. 290–300. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11758-4_32
Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: Advances in Neural Information Processing Systems, pp. 153–160 (2007)
Berndt, D., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, vol. 10, no. 16, pp. 359–370 (1994)
Deng, L., Yu, D.: Deep learning for signal and information processing. Found. Trends Signal Process. 2–3, 197–387 (2013)
Ding, X., Zhang, Y., Liu, T., Duan, J.: Deep learning for event-driven stock prediction. In: Proceedings of the 24th International Conference on Artificial Intelligence, pp. 2327–2333. AAAI Press (2015)
Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Transfer learning for time series classification. In: 2018 IEEE International Conference on Big Data (Big Data). pp. 1367–1376. IEEE (2018)
Gardner Jr., E.S.: Exponential smoothing: the state of the art. Int. J. Forecast. 4(1), 1–28 (1985)
Gardner Jr., E.S.: Exponential smoothing: the state of the art–part ii. Int. J. Forecast. 22(4), 637–666 (2006)
Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 513–520 (2011)
Haque, A.U., Nehrir, M.H., Mandal, P.: A hybrid intelligent model for deterministic and quantile regression approach for probabilistic wind power forecasting. IEEE Trans. Power Syst. 29(4), 1663–1672 (2014)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Hu, Q., Zhang, R., Zhou, Y.: Transfer learning for short-term wind speed prediction with deep neural networks. Renew. Energy 85, 83–95 (2016)
Huang, J.T., Li, J., Yu, D., Deng, L., Gong, Y.: Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 7304–7308. IEEE (2013)
Hyndman, R., Khandakar, Y.: Automatic time series forecasting: the forecast package for R. J. Stat. Softw. 27(3), 1–22 (2008)
Karlik, B., Olgac, A.V.: Performance analysis of various activation functions in generalized mlp architectures of neural networks. Int. J. Artif. Intell. Expert Syst. 1(4), 111–122 (2011)
Makridakis, S., Spiliotis, E., Assimakopoulos, V.: Statistical and machine learning forecasting methods: Concerns and ways forward. PLOS One 13(3), 1–26 (2018)
Ortiz-García, E.G., Salcedo-Sanz, S., Pérez-Bellido, Á.M., Gascón-Moreno, J., Portilla-Figueras, J.A., Prieto, L.: Short-term wind speed prediction in wind farms based on banks of support vector machines. Wind Energy 14(2), 193–207 (2011)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009)
Ramachandran, P., Liu, P., Le, Q.: Unsupervised pretraining for sequence to sequence learning. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 383–391. Association for Computational Linguistics (2017)
Rosenstein, M.T., Marx, Z., Kaelbling, L.P., Dietterich, T.G.: To transfer or not to transfer. In: NIPS 2005 Workshop on Transfer Learning, vol. 898, pp. 1–4 (2005)
Vu, N.T., Imseng, D., Povey, D., Motlicek, P., Schultz, T., Bourlard, H.: Multilingual deep neural network based acoustic modeling for rapid language adaptation. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7639–7643. IEEE (2014)
Wang, B., Huang, H., Wang, X.: A novel text mining approach to financial time series forecasting. Neurocomputing 83, 136–145 (2012)
Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3(1), 9 (2016). https://doi.org/10.1186/s40537-016-0043-6
Wu, D.D., Olson, D.L.: Financial risk forecast using machine learning and sentiment analysis. In: Wu, D.D., Olson, D.L. (eds.) Enterprise Risk Management in Finance, pp. 32–48. Springer, London (2015). https://doi.org/10.1057/9781137466297_5
Ye, R., Dai, Q.: A novel transfer learning framework for time series forecasting. Knowl. Based Syst. 156, 74–99 (2018)
Yoshihara, A., Fujikawa, K., Seki, K., Uehara, K.: Predicting stock market trends by recurrent deep neural networks. In: Pham, D.-N., Park, S.-B. (eds.) PRICAI 2014. LNCS (LNAI), vol. 8862, pp. 759–769. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13560-1_60
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320–3328 (2014)
Acknowledgement
The research was funded by the Research Committee of University of Macau, Grant MYRG2018-00246-FST.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
He, QQ., Pang, P.CI., Si, YW. (2019). Transfer Learning for Financial Time Series Forecasting. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11671. Springer, Cham. https://doi.org/10.1007/978-3-030-29911-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-29911-8_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29910-1
Online ISBN: 978-3-030-29911-8
eBook Packages: Computer ScienceComputer Science (R0)