Abstract
IoT data, that most often carry a temporal dimension, can be exploited from an analysis perspective or from a forecasting one. In this paper, we propose a predictive approach to address the problem of data trustworthiness in a data stream generated by a Smart Home application. We describe an online Ensemble Regression model that performs prediction in assigning a trust score to a target temporal value in real-time. Experiments conducted with data retrieved from the UCI ML repository demonstrate the performance of the model, while assessing data accuracy.
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Notes
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Details about Page-Hinkley method for concept drift detection are available at https://scikit-multiflow.github.io/scikit-multiflow/.
- 3.
Available in Sklearn: https://scikit-learn.org/stable/.
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Peng, T., Sellami, S., Boucelma, O. (2020). Trust Assessment on Streaming Data: A Real Time Predictive Approach. In: Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R., Ifrim, G. (eds) Advanced Analytics and Learning on Temporal Data. AALTD 2020. Lecture Notes in Computer Science(), vol 12588. Springer, Cham. https://doi.org/10.1007/978-3-030-65742-0_14
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