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Actually, many updating mechanism for concept drifting data streams have been proposed.
Experimental evaluation of the framework, on generated adversarial drifting data streams, demonstrates that this framework is able to provide early and reliable unsupervised indication of drift, and is able to recover from drifts swiftly.
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In addition, forecast results are compared to drift data collected during OATRC 2015.
For the previous reasons, we focus in this article on the internal clustering validation and study its usability for drifting streaming data.
The common problems encountered in SOC estimations, such as local model inaccuracy, current sensor drifting and data saturation, could be overcome.
In addition, we also apply the OTL technique to attack the challenging online learning tasks with concept-drifting data streams.
This paper presents an initial investigation of preliminary ion drift data made available by the European Space Agency.
First, the temperature drift data for modeling is preprocessed by adaptive forward linear prediction (FLP) filter.
These results indicate there is an offset between the Swarm ion drift data and the actual drifts.
To illustrate the proposed procedure for correcting Swarm ion drift data, we consider a single cross-polar satellite pass.
In (c) and (d) the horizontal black lines indicate the offset used to correct Swarm ion drift data.
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