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 / Amato, A.; Calabrese, M.; DI LECCE, Vincenzo. - (2008), pp. 895-899. (Intervento presentato al convegno 25th IEEE Instrumentation and Measurement Technology Conference tenutosi a Victoria, BC, Canada nel May 12-15, 2008) [10.1109/IMTC.2008.4547163].
Decision trees in time series reconstruction problems
DI LECCE, Vincenzo
2008-01-01
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 casesI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
