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Big data techniques have been applied to power grid for the prediction and evaluation of grid conditions. However, the raw data quality can rarely meet the requirement of precise data analytics since raw data set usually contains samples with missing data to which the common data mining models are sensitive. Besides, the raw training data from a single monitoring system, e.g. dissolved gas analysis...
Data missing in collections of time series occurs frequently in practical applications and turns out to be a major menace to precise data analysis. However, most of the existing methods either might be infeasible or could be inefficient to predict the missing values in large-scale coevolving time series. Also, the evolving of time series needs to be handled properly to adapt to the temporal characteristic...
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