Lag Correlation Discovery and Classification for Time Series

Georgios Dimitropoulos, Estela Papagianni, Vasileios Megalooikonomou

2017

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.

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