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Integrated Forecast of Financial Time Series Data Based on Deep Learning Model2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)10.1109/TOCS56154.2022.10016211
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SVR Modeling and Parameter Optimization for Financial Time Series Forecasting2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)10.1109/TOCS56154.2022.10016054

Exploiting Visual Features in Financial Time Series Prediction

Adil Gürsel Karaçor, Turan Erman Erkan

Source Title: International Journal of Cognitive Informatics and Natural Intelligence (IJCINI)14(2)

ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799805328|DOI: 10.4018/IJCINI.2020040104

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MLA

Karaçor, Adil Gürsel, and Turan Erman Erkan. "Exploiting Visual Features in Financial Time Series Prediction." IJCINI vol.14, no.2 2020: pp.61-76. http://doi.org/10.4018/IJCINI.2020040104

APA

Karaçor, A. G. & Erkan, T. E. (2020). Exploiting Visual Features in Financial Time Series Prediction. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 14(2), 61-76. http://doi.org/10.4018/IJCINI.2020040104

Chicago

Karaçor, Adil Gürsel, and Turan Erman Erkan. "Exploiting Visual Features in Financial Time Series Prediction," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 14, no.2: 61-76. http://doi.org/10.4018/IJCINI.2020040104

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Abstract

The possibility to enhance prediction accuracy for foreign exchange rates was investigated in two ways: first applying an outside the box approach to modeling price graphs by exploiting their visual properties, and secondly employing the most efficient methods to detect patterns to classify the direction of movement. The approach that exploits the visual properties of price graphs which make use of density regions along with high and low values describing the shape; hence, the authors propose the name ‘Finance Vision.' The data used in the predictive model consists of 1-hour past price values of 4 different currency pairs, between 2003 and 2016. Prediction performances of state-of-the-art methods; Extreme Gradient Boosting, Artificial Neural Network and Support Vector Machines are compared over the same data with the same sets of features. Results show that density based visual features contribute considerably to prediction performance.