A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 7 Issue 3
Apr.  2020

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
Silvio Barra, Salvatore Mario Carta, Andrea Corriga, Alessandro Sebastian Podda and Diego Reforgiato Recupero, "Deep Learning and Time Series-to-Image Encoding for Financial Forecasting," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 683-693, May 2020. doi: 10.1109/JAS.2020.1003132
Citation: Silvio Barra, Salvatore Mario Carta, Andrea Corriga, Alessandro Sebastian Podda and Diego Reforgiato Recupero, "Deep Learning and Time Series-to-Image Encoding for Financial Forecasting," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 683-693, May 2020. doi: 10.1109/JAS.2020.1003132

Deep Learning and Time Series-to-Image Encoding for Financial Forecasting

doi: 10.1109/JAS.2020.1003132
Funds:  This work was supported by the “Bando Aiuti per progetti di Ricerca e Sviluppo-POR FESR 2014-2020-Asse 1, Azione 1.1.3. Project AlmostAnOracle-AI and Big Data Algorithms for Financial Time Series Forecasting”
More Information
  • In the last decade, market financial forecasting has attracted high interests amongst the researchers in pattern recognition. Usually, the data used for analysing the market, and then gamble on its future trend, are provided as time series; this aspect, along with the high fluctuation of this kind of data, cuts out the use of very efficient classification tools, very popular in the state of the art, like the well known convolutional neural networks (CNNs) models such as Inception, ResNet, AlexNet, and so on. This forces the researchers to train new tools from scratch. Such operations could be very time consuming. This paper exploits an ensemble of CNNs, trained over Gramian angular fields (GAF) images, generated from time series related to the Standard & Poor’s 500 index future; the aim is the prediction of the future trend of the U.S. market. A multi-resolution imaging approach is used to feed each CNN, enabling the analysis of different time intervals for a single observation. A simple trading system based on the ensemble forecaster is used to evaluate the quality of the proposed approach. Our method outperforms the buy-and-hold (B&H) strategy in a time frame where the latter provides excellent returns. Both quantitative and qualitative results are provided.


  • loading
  • [1]
    T. Kimoto, K. Asakawa, M. Yoda, and M. Takeoka, “Stock market prediction system with modular neural networks,” in Proc. Int. Joint Conf. Neural Networks, San Diego, CA, USA, 1990, pp. 1–6.
    Y. D. Zhang and L. E. Wu, “Stock market prediction of S&P500 via combination of improved BCO approach and BP neural network,” Expert Syst. Appl., vol. 36, no. 5, pp. 8849–8854, Jul. 2009. doi: 10.1016/j.eswa.2008.11.028
    T. Z. Tan, C. Quek, and G. S. Ng, “Brain-inspired genetic complementary learning for stock market prediction,” in Proc. IEEE Congr. Evolutionary Computation, Edinburgh, Scotland, UK, 2005, pp. 2653–2660.
    S. Soni, “Applications of ANNs in stock market prediction: a survey,” Int. J. Comput. Sci. Eng. Technol., vol. 2, no. 3, pp. 71–83, 2011.
    T. Lintonen and T. Raty, “Self-learning of multivariate time series using perceptually important points,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1318–1331, Nov. 2019. doi: 10.1109/JAS.2019.1911777
    K. Kamijo and T. Tanigawa, “Stock price pattern recognition-a recurrent neural network approach,” in Proc. Int. Joint Conf. Neural Networks, San Diego, CA, USA, 1990, pp. 215–221.
    C. H. Lee and K. C. Park, “Prediction of monthly transition of the composition stock price index using recurrent back-propagation,” in Artificial Neural Networks, I. Aleksander and J. Taylor, Eds. Amsterdam, Netherlands: Elsevier, 1992, pp. 1629–1632.
    E. Guresen, G. Kayakutlu, and T. U. Daim, “Using artificial neural network models in stock market index prediction,” Expert Syst. Appl., vol. 38, no. 8, pp. 10389–10397, Aug. 2011. doi: 10.1016/j.eswa.2011.02.068
    S. C. Gao, M. C. Zhou, Y. R. Wang, J. J. Cheng, H. Yachi, and J. H. Wang, “Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 2, pp. 601–614, Feb. 2019. doi: 10.1109/TNNLS.2018.2846646
    D. B. Jia, S. X. Zheng, L. Yang, Y. Todo, and S. C. Gao, “A dendritic neuron model with nonlinearity validation on Istanbul stock and Taiwan futures exchange indexes prediction,” in Proc. 5th IEEE Int. Conf. Cloud Computing and Intelligence Systems, Nanjing, China, 2018, pp. 242–246.
    T. L. Zhou, S. C. Gao, J. H. Wang, C. Y. Chu, Y. Todo, and Z. Tang, “Financial time series prediction using a dendritic neuron model,” Knowl-Based Syst., vol. 105, pp. 214–224, Aug. 2016. doi: 10.1016/j.knosys.2016.05.031
    J. D. Farmer and A. W. Lo, “Frontiers of finance: evolution and efficient markets,” Proc. Natl. Acad. Sci. USA, vol. 96, no. 18, pp. 9991–9992, Aug. 1999. doi: 10.1073/pnas.96.18.9991
    V. S. Pagolu, K. N. Reddy, G. Panda, and B. Majhi, “Sentiment analysis of twitter data for predicting stock market movements,” in Proc. Int. Conf. Signal Processing, Communication, Power and Embedded System, Paralakhemundi, India, 2016, pp. 1345–1350.
    A. Mittal and A. Goel, “Stock prediction using twitter sentiment analysis,” 2012. [Online]. Available:http://cs229.stanford.edu/proj2011/GoelMittal-StockMarketPredictionUsingTwitterSentimentAnalysis.pdf
    N. Oliveira, P. Cortez, and N. Areal, “The impact of microblogging data for stock market prediction: using twitter to predict returns, volatility, trading volume and survey sentiment indices,” Expert Syst. Appl., vol. 73, pp. 125–144, May 2017. doi: 10.1016/j.eswa.2016.12.036
    T. B. Trafalis and H. Ince, “Support vector machine for regression and applications to financial forecasting,” in Proc. IEEE-INNS-ENNS Int. Joint Conf. Neural Networks 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, Como, Italy, 2000, pp. 348–353.
    C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., vol. 20, no. 3, pp. 273–297, Sept. 1995.
    B. M. Henrique, V. A. Sobreiro, and H. Kimura, “Literature review: machine learning techniques applied to financial market prediction,” Expert Syst. Appl., vol. 124, pp. 226–251, Jun. 2019. doi: 10.1016/j.eswa.2019.01.012
    C. F. Tsai and S. P. Wang, “Stock price forecasting by hybrid machine learning techniques,” in Proc. Int. MultiConf. Engineers and Computer Scientists, Hong Kong, China, 2009, pp. 60.
    J. Patel, S. Shah, P. Thakkar, and K. Kotecha, “Predicting stock market index using fusion of machine learning techniques,” Expert Syst. Appl., vol. 42, no. 4, pp. 2162–2172, Mar. 2015. doi: 10.1016/j.eswa.2014.10.031
    D. Shah, H. Isah, and F. Zulkernine, “Stock market analysis: a review and taxonomy of prediction techniques,” Int. J. Financ. Stud., vol. 7, no. 2, pp. 26, Jun. 2019. doi: 10.3390/ijfs7020026
    M. Ballings, D. van den Poel, N. Hespeels, and R. Gryp, “Evaluating multiple classifiers for stock price direction prediction,” Expert Syst. Appl., vol. 42, no. 20, pp. 7046–7056, Nov. 2015. doi: 10.1016/j.eswa.2015.05.013
    S. Basak, S. Kar, S. Saha, L. Khaidem, and S. R. Dey, “Predicting the direction of stock market prices using tree-based classifiers,” North Am. J. Econ. Finance, vol. 47, pp. 552–567, Jan. 2019. doi: 10.1016/j.najef.2018.06.013
    S. Dey, Y. Kumar, S. Saha, and S. Basak, “Forecasting to classification: predicting the direction of stock market price using Xtreme gradient boosting,” 2016. [Online]. Available: https://doi.org/10.13140/RG.2.2.15294.48968
    S. K. Aggarwal, L. M. Saini, and A. Kumar, “Price forecasting using wavelet transform and LSE based mixed model in Australian electricity market,” Int. J. Energy Sector Manage., vol. 2, no. 4, pp. 521–546, Nov. 2008. doi: 10.1108/17506220810919054
    P. M. Kebria, A. Khosravi, S. M. Salaken, and S. Nahavandi, “Deep imitation learning for autonomous vehicles based on convolutional neural networks,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 82–95, Jan. 2020. doi: 10.1109/JAS.2019.1911825
    D. Freire-Obregón, F. Narducci, S. Barra, and M. Castrillón-Santana, “Deep learning for source camera identification on mobile devices,” Pattern Recognit. Lett., vol. 126, pp. 86–91, Sept. 2019. doi: 10.1016/j.patrec.2018.01.005
    Z. G. Wang and T. Oates, “Imaging time-series to improve classification and imputation,” in Proc. 24th Int. Joint Conf. Artificial Intelligence, 2015.
    G. G. Calvi, V. Lucic, and D. P. Mandic, “Support tensor machine for financial forecasting,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, Brighton, United Kingdom, 2019, pp. 8152–8156.
    Z. G. Wang and T. Oates, “Encoding time series as images for visual inspection and classification using tiled convolutional neural networks,” in Proc. Workshops at the 29th AAAI Conf. Artificial Intelligence, 2015.
    K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv: 1409.1556, 2014.
    K. M. He, X. Y. Zhang, S. Q. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 770–778.
    P. Cizeau, Y. H. Liu, M. Meyer, C. K. Peng, and H. E. Stanley, “Volatility distribution in the S&P500 stock index,” Phys. A Stat. Mech. Appl., vol. 245, no. 3–4, pp. 441–445, Nov. 1997. doi: 10.1016/S0378-4371(97)00417-2
    M. Martens, “Measuring and forecasting S&P500 index-futures volatility using high-frequency data,” J. Futur. Mark., vol. 22, no. 6, pp. 497–518, Jun. 2002. doi: 10.1002/fut.10016


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(3)

    Article Metrics

    Article views (6960) PDF downloads(697) Cited by()


    • The application of Gramian Angular Field Imaging on time series helps the improving market prediction results.
    • A multi-resolution structure is adopted, in order to resample the time series using different time granularity.
    • The classification phase is organized in an ensemble of a set of Convolutional Neural Networks, each initialized with a different kernel function; a walk-forward validation approach is applied.
    • Comparison with the state of the art approaches is performed; the results show that the proposed approach is capable of obtaining a higher profit in the same investment period.


    DownLoad:  Full-Size Img  PowerPoint