This work proposes to use a decision tree classifier for time series data reconstruction. Object of this analysis is to study environmental data acquired by a distributed multi-sensors monitoring system placed in Taranto. The performance obtained in data reconstruction using the proposed decision tree is compared with those obtained using two well known signal reconstruction methods: mean value and polynomial interpolation. The results show that the decision tree outperforms the other two methods in almost all the analyzed cases.
Decision Trees in Time Series Reconstruction Problems / A. Amato, M. Calabrese, V. Di Lecce - In: Instrumentation and Measurement Technology Conference Proceedings, 2008. IMTC 2008. IEEE[s.l] : IEEE, 2008 May 12. - ISBN 978-1-4244-1540-3. - pp. 895-899 (( convegno IEEE International Instrumentation and Measurement Technology Conference tenutosi a Vancouver nel 2008.
Decision Trees in Time Series Reconstruction Problems
A. AmatoPrimo
;M. CalabreseSecondo
;
2008
Abstract
This work proposes to use a decision tree classifier for time series data reconstruction. Object of this analysis is to study environmental data acquired by a distributed multi-sensors monitoring system placed in Taranto. The performance obtained in data reconstruction using the proposed decision tree is compared with those obtained using two well known signal reconstruction methods: mean value and polynomial interpolation. The results show that the decision tree outperforms the other two methods in almost all the analyzed cases.
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