I am working on a stream learning of a univariate timeseries data collected from a sensor every hour with the seasonality of a week. The input data might be missing for more than a week completely at random because of sensor malfuncion. Therefore, I need a strategy to impute the missing data before the data is given to the actual stream learning algorithm.
I thought of following solutions :
Use Averaging or Kalman filters for filling. But they are not good for filling up large set of continuous missing points.
Use the model prepared by forecasting algorithm to generate forecasted data. Use this data to impute the missing data. And feed the same imputed timeseries to forecasting algorithm. Can this approach cause trouble?
Any pointers for imputation will be helpful for stream learning.