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

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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”
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  • 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.


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    • 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.


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