Abstract
In this study, we propose a deeper analysis on the algorithmic treatment of financial time series, with a focus on Forex markets’ applications. The relevant aspects of the paper refers to a more beneficial data arrangement, proposed into a two-dimensional objects and to the application of a Temporal Convolutional Neural Network model, representing a more than valid alternative to Recurrent Neural Networks. The results are supported by expanding the comparison to other more consolidated deep learning models, as well as with some of the most performing Machine Learning methods. Finally, a financial framework is proposed to test the real effectiveness of the algorithms.
A. Cuzzocrea—This research has been made in the context of the Excellence Chair in Computer Engineering – Big Data Management and Analytics at LORIA, Nancy, France.
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References
Ahmed, N.K., Atiya, A.F., El Gayar, N., El-Shishiny, H.: An empirical comparison of machine learning models for time series forecasting. Economet. Rev. 29(5–6), 594–621 (2010)
Bai, S., Zico Kolter, J., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. CoRR, abs/1803.01271 (2018)
Campan, A., Cuzzocrea, A., Truta, T.M.: Fighting fake news spread in online social networks: actual trends and future research directions. In: 2017 IEEE International Conference on Big Data, BigData 2017, Boston, MA, USA, 11–14 December 2017, pp. 4453–4457. IEEE Computer Society (2017)
Castrogiovanni, P., Fadda, E., Perboli, G., Rizzo, A.: Smartphone data classification technique for detecting the usage of public or private transportation modes. IEEE Access 8, 58377–58391 (2020)
Ceci, M., Cuzzocrea, A., Malerba, D.: Effectively and efficiently supporting roll-up and drill-down OLAP operations over continuous dimensions via hierarchical clustering. J. Intell. Inf. Syst. 44(3), 309–333 (2015). https://doi.org/10.1007/s10844-013-0268-1
Cuzzocrea, A., Leung, C.K., Deng, D., Mai, J.J., Jiang, F., Fadda, E.: A combined deep-learning and transfer-learning approach for supporting social influence prediction. Procedia Comput. Sci. 177, 170–177 (2020)
Cuzzocrea, A., Song, I.Y.: Big graph analytics: the state of the art and future research agenda. In: Proceedings of the 17th International Workshop on Data Warehousing and OLAP, DOLAP 2014, Shanghai, China, 3–7 November 2014, pp. 99–101. ACM (2014)
Evans, C., Pappas, K., Xhafa, F.: Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation. Math. Comput. Model. 58(5), 1249–1266 (2013)
GastĂłn, M., FrĂas, L., Fernández-Peruchena, C.M., Mallor, F.: The temporal distortion index (TDI). A new procedure to analyze solar radiation forecasts. In: AIP Conference Proceedings, vol. 1850, p. 140009 (2017)
Krollner, B., Vanstone, B.J., Finnie, G.R.: Financial time series forecasting with machine learning techniques: a survey. In: ESANN 2010, 18th European Symposium on Artificial Neural Networks, Bruges, Belgium, 28–30 April 2010, Proceedings (2010)
Le Guen, V., Thome, N.: Shape and time distortion loss for training deep time series forecasting models. In: Advances in Neural Information Processing Systems, vol. 32. Curran Associates Inc. (2019)
Li, H., Yang, L.: Accurate and fast dynamic time warping. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds.) ADMA 2013. LNCS (LNAI), vol. 8346, pp. 133–144. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-53914-5_12
Li, Y., Fadda, E., Manerba, D., Tadei, R., Terzo, O.: Reinforcement learning algorithms for online single-machine scheduling. In: 2020 15th Conference on Computer Science and Information Systems (FedCSIS), pp. 277–283 (2020)
Manibardo, E.L., Laña, I., Del Ser, J.: Deep learning for road traffic forecasting: does it make a difference? IEEE Trans. Intell. Transp. Syst. pp. 1–25 (2021)
Rivest, F., Kohar, R.: A new timing error cost function for binary time series prediction. IEEE Trans. Neural Netw. Learn. Syst. 31(1), 174–185 (2020)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. CoRR, abs/1606.03498 (2016)
Omer Berat Sezer and Ahmet Murat Ozbayoglu: Algorithmic financial trading with deep convolutional neural networks: time series to image conversion approach. Appl. Soft Comput. 70, 525–538 (2018)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(56), 1929–1958 (2014)
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Baldo, A., Cuzzocrea, A., Fadda, E., Bringas, P.G. (2021). Financial Forecasting via Deep-Learning and Machine-Learning Tools over Two-Dimensional Objects Transformed from Time Series. In: Sanjurjo González, H., Pastor LĂłpez, I., GarcĂa Bringas, P., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2021. Lecture Notes in Computer Science(), vol 12886. Springer, Cham. https://doi.org/10.1007/978-3-030-86271-8_46
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