# Combining Piecewise Linear Regression and a Granular Computing Framework for Financial Time Series Classification

### Valerio Modugno, Francesca Possemato, Antonello Rizzi

#### Abstract

Finance is a very broad field where the uncertainty plays a central role and every financial operator have to deal with it. In this paper we propose a new method for a trend prediction on financial time series combining a Linear Piecewise Regression with a granular computing framework. A set of parameters control the behavior of the whole system, thus making their fine tuning a critical optimization task. To this aim in this paper we employ an evolutionary optimization algorithm to tackle this crucial phase. We tested our system on both synthetic benchmarking data and on real financial time series. Our tests show very good classification results on benchmarking data. Results on real data, although not completely satisfactory, are encouraging, suggesting further developments.

#### References

- Bagheri, A., Mohammadi Peyhani, H., and Akbari, M. (2014). Financial forecasting using anfis networks with quantum-behaved particle swarm optimization. 41:6235-6250.
- Bargiela, A. and Pedrycz, W. (2003). Granular computing: an introduction. Springer.
- Berndt, D. J. and Clifford, J. (1994). Using dynamic time warping to find patterns in time series. In KDD workshop, volume 10, pages 359-370. Seattle, WA.
- Chang, P.-C., Fan, C.-Y., and Liu, C.-H. (2009). Integrating a piecewise linear representation method and a neural network model for stock trading points prediction. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 39(1):80-92.
- Cheng, C.-H., Su, C.-H., Chen, T.-L., and Chiang, H.-H. (2010). Forecasting stock market based on price trend and variation pattern. 5990:455-464.
- Haugen, R. A. (1999). The new finance: the case against efficient markets, volume 2. Prentice Hall Upper Saddle River.
- Kendall, G. and Su, Y. (2005). A particle swarm optimisation approach in the construction of optimal risky portfolios. In Artificial Intelligence and Applications, pages 140-145. Citeseer.
- Livi, L., Del Vescovo, G., and Rizzi, A. (2012). Graph recognition by seriation and frequent substructures mining. In ICPRAM, pages 186-191.
- Los, C. A. (2000). Nonparametric efficiency testing of asian stock markets using weekly data. Advances in Econometrics, 14:329-363.
- Malkiel, B. G. and Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2):383-417.
- Muggeo, V. M. (2003). Estimating regression models with unknown break-points. Statistics in medicine, 22(19):3055-3071.
- Nanni, L. (2006). Multi-resolution subspace for financial trading. Pattern recognition letters, 27(2):109-115.
- Possemato, F. and Rizzi, A. (2013). Automatic text categorization by a granular computing approach: Facing unbalanced data sets. In Neural Networks (IJCNN), The 2013 International Joint Conference on, pages 1- 8. IEEE.
- Radeerom, M., Wongsuwarn, H., and Kasemsan, M. L. K. (2012). Intelligence decision trading systems for stock index. 7198:366-375.
- Rizzi, A., Del Vescovo, G., Livi, L., and Mascioli, F. M. F. (2012). A new granular computing approach for sequences representation and classification. In Neural Networks (IJCNN), The 2012 International Joint Conference on, pages 1-8. IEEE.
- Rizzi, A., Possemato, F., Livi, L., Sebastiani, A., Giuliani, A., and Mascioli, F. M. F. (2013). A dissimilaritybased classifier for generalized sequences by a granular computing approach. In Neural Networks (IJCNN), The 2013 International Joint Conference on, pages 1-8. IEEE.
- Sai, Y., Yuan, Z., and Gao, K. (2007). Mining stock market tendency by rs-based support vector machines. In Granular Computing, 2007. GRC 2007. IEEE International Conference on, pages 659-659. IEEE.
- Vanstone, B. and Tan, C. (2003). A survey of the application of soft computing to investment and financial trading. Information Technology papers, page 13.

#### Paper Citation

#### in Harvard Style

Modugno V., Possemato F. and Rizzi A. (2014). **Combining Piecewise Linear Regression and a Granular Computing Framework for Financial Time Series Classification** . In *Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)* ISBN 978-989-758-052-9, pages 281-288. DOI: 10.5220/0005127402810288

#### in Bibtex Style

@conference{ecta14,

author={Valerio Modugno and Francesca Possemato and Antonello Rizzi},

title={Combining Piecewise Linear Regression and a Granular Computing Framework for Financial Time Series Classification},

booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)},

year={2014},

pages={281-288},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0005127402810288},

isbn={978-989-758-052-9},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)

TI - Combining Piecewise Linear Regression and a Granular Computing Framework for Financial Time Series Classification

SN - 978-989-758-052-9

AU - Modugno V.

AU - Possemato F.

AU - Rizzi A.

PY - 2014

SP - 281

EP - 288

DO - 10.5220/0005127402810288