Abstract
In this paper, several classification methods are applied for modeling financial time series with the aim to predict the trend of successive prices. By using a suitable embedding technique, a pattern of past prices is assigned a class if the variation of the next price is over, under or stable with respect to a given threshold. Furthermore, a sensitivity analysis is performed in order to verify if the value of such a threshold influences the prediction accuracy. The experimental results on the case study of WTI crude oil commodity show a good classification accuracy of the next (predicted) trend, and the best performance is achieved by the K-Nearest Neighbors classification strategy.
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Altilio, R., Andreasi, G., Panella, M. (2019). A Classification Approach to Modeling Financial Time Series. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Advances in Processing Nonlinear Dynamic Signals. WIRN 2017 2017. Smart Innovation, Systems and Technologies, vol 102. Springer, Cham. https://doi.org/10.1007/978-3-319-95098-3_9
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