A Versatile Approach to Classification of Multivariate Time Series Data

• Autorzy: Adam Zagorecki
• Języki publikacji: EN

Abstrakty

EN

During the recent decade we have experienced a rise of popularity of sensors capable of collecting large amounts of data. One of most popular types of data collected by sensors is time series composed of sequences of measurements taken over time. With low cost of individual sensors, multivariate time series data sets are becoming common. Examples can include vehicle or machinery monitoring, sensors from smartphones or sensor suites installed on a human body. This paper describes a generic method that can be applied to arbitrary set of multivariate time series data in order to perform classification or regression tasks. This method was applied to the 2015 AAIA Data Mining Competition concerned with classifying firefighter activities and consecutively led to achieving the second-high score of nearly 80 participant teams.(original abstract)

Słowa kluczowe

EN

Time-series

PL

Szeregi czasowe

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2015
• Tom: 5
• Strony: 407--410

Twórcy

autor

Adam Zagorecki

• Centre for Simulation and Analytics Cranfield University Defence Academy of the United Kingdom

Bibliografia

• ] Meina, M., Janusz, A., Rykaczewski, K., Ślęzak, D., Celmer, B., and Krasuski, A., "Tagging Firefighter Activities at the Emergency Scene: Summary of AAIAâA˘ Z15 Data Mining Competition at Knowledge Pit", ´ Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, 2015.
• Janusz, A., Krasuski, A., Stawicki, S., Rosiak, M., Slezak, D., and Hung Son Nguyen, "Key risk factors for Polish State Fire Service: A Data Mining Competition at Knowledge Pit," Computer Science and Information Systems (FedCSIS), 2014 Federated Conference on pp.345- 354, 7-10 Sept. 2014, doi: 10.15439/2014F507.
• Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H., "The WEKA Data Mining Software: An Update", SIGKDD Explorations, Volume 11, Issue 1. 2009.
• Hall, M. A., "Correlation-based Feature Subset Selection for Machine Learning". Hamilton, New Zealand. 1998.
• Breiman, L., "Random Forests", Machine Learning, Volume 45, Issue 1, pp. 5-32. October 2001.

Identyfikatory

DOI

10.15439/2015F419