Tackling Missing Values in Probabilistic Wind Power Forecasting: A
  Generative Approach
Honglin Wen Pierre Pinson Jie Gu Zhijian Jin
Machine learning techniques have been successfully used in probabilistic wind
power forecasting. However, the issue of missing values within datasets due to
sensor failure, for instance, has been overlooked for a long time. Although it
is natural to consider addressing this issue by imputing missing values before
model estimation and forecasting, we suggest treating missing values and
forecasting targets indifferently and predicting all unknown values
simultaneously based on observations. In this paper, we offer an efficient
probabilistic forecasting approach by estimating the joint distribution of
features and targets based on a generative model. It is free of preprocessing,
and thus avoids introducing potential errors. Compared with the traditional
"impute, then predict" pipeline, the proposed approach achieves better
performance in terms of continuous ranked probability score.