# Lag Correlation Discovery and Classification for Time Series

### Georgios Dimitropoulos, Estela Papagianni, Vasileios Megalooikonomou

#### Abstract

Time series data are ubiquitous and their analysis necessitates the use of effective data mining methods to aid towards decision making. The mining problems that are studied in this paper are lag correlation discovery and classification. For the first problem, a new lag correlation algorithm for time series, the Highly Sparse Lag Correlation (HSLC) is proposed. This algorithm is a combination of Boolean Lag Correlation (BLC) and Hierarchical Boolean Representation (HBR) algorithms and aims to improve the time performance of Pearson Lag Correlation (PLC) algorithm. The classification algorithm that is employed for data streams is an incremental support vector machine (SVM) learning algorithm. To verify the effectiveness and efficiency of the proposed schemes, the lag correlation discovery algorithm is experimentally tested on electroencephalography (EEG) data, whereas the classification algorithm that operates on streams is tested on real financial data. The HSLC algorithm achieves better time performance than previous state-of-the-art methods such as the PLC algorithm and the incremental SVM learning algorithm that we adopt, increases the accuracy achieved by non-incremental models.

#### References

- Diehl, C. P. and Cauwenberghs, C. (2003) SVM Incremental Learning, Adaption and Optimization. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 2685-2690.
- Edwards, R.D., Magee, J. and Bassetti, W.H.C. (2007) Technical Analysis of Stock Trends, 9th ed.
- Kim, Kyoung-jae (2003) Financial time series forecasting using support vector machines. Neurocomputing Journal, 55(1-2), pp. 307-319.
- Mporas I., Tsirka, V., Zacharaki, E. I., Koutroumanidis, M., Richardson, M., Megalooikonomou, V. (2015) Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients. Expert Systems with Applications, 42(6), pp. 3227-3233.
- Poggio, T. and Cauwenberghs, C. (2001) Incremental and Decremental Support Vector Machine Learning. In: Proceedings of the 2000 Conference on Advances in Neural Information Processing Systems, Vol. 13, MIT Press.
- Shin, K.S., Lee, T.S. and Kim, H. (2005) An application of support vector machines in bankruptcy model. Expert Systems with Applications, 28(1), pp. 127-135.
- Syed, N. A., Liu, H. and Sung, K. (1999) Incremental Learning with Support Vector Machines. In: Proceedings of the International Joint Conference on Artificial Intelligence.
- Yi, B.-K., Sidiropoulos, N.D., Johnson, T., Jagadish, H.V., Faloutsos, C., Biliris, A. (2000) Online Data Mining for Co-Evolving Time Sequences. In Proceedings of the 16th International Conference on Data Engineering, pp. 13-22.
- Zhang, T., Yue, D., Gu, Y., Wang, Y. and Yu, G. (2009) Adaptive correlation analysis in stream time series with sliding windows. Computers & Mathematics with Applications, 57(6), pp. 937-948.
- Zhang, T., Yue, D., Wang, Y. and Yu, G. (2011) A Novel Approach for Mining Multiple Data Streams based on Lag Correlation. In: Proceedings of the Control and Decision Conference, pp. 2377-2382.
- Zhu, Y. and Shasha, D. (2002) StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time: In: Proceedings of the 28th International Conference on Very Large Data Bases, pp. 358-369.
- Google/Finance. (2014). [Online] Available from: http://www.google.com/finance. [Accessed 14/3/17].
- Microsoft StreamInsight. (2016). [Software] [Online]Available from: https://technet.microsoft.com/ enus/library/ee362541.
- Diehl, C. (2011). Github, inc. [Online] Available from: https://github.com/diehl/Incremental-SVM-Learningin-MATLAB.

#### Paper Citation

#### in Harvard Style

Dimitropoulos G., Papagianni E. and Megalooikonomou V. (2017). **Lag Correlation Discovery and Classification for Time Series** . In *Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,* ISBN 978-989-758-245-5, pages 181-188. DOI: 10.5220/0006215901810188

#### in Bibtex Style

@conference{iotbds17,

author={Georgios Dimitropoulos and Estela Papagianni and Vasileios Megalooikonomou},

title={Lag Correlation Discovery and Classification for Time Series},

booktitle={Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},

year={2017},

pages={181-188},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0006215901810188},

isbn={978-989-758-245-5},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,

TI - Lag Correlation Discovery and Classification for Time Series

SN - 978-989-758-245-5

AU - Dimitropoulos G.

AU - Papagianni E.

AU - Megalooikonomou V.

PY - 2017

SP - 181

EP - 188

DO - 10.5220/0006215901810188