Stochastic regression imputation can be considered a refinement of regression imputation because it addresses the correlation bias by adding noise from the regression residuals to the missing value estimations. This post discusses the advantages of stochastic regression imputation with examples in Python.
Deploying descriptive, predictive and prescriptive machine learning solution using complete data is difficult, but even more difficult in face of missing data. A gentle introduction to the reasons of missing data and the difficulties generated.
Missing data, especially missing data points in time series, are a pervasive issue for many applications relying on precise and complete data sets. For example in the financial sector, missing tick data can lead to deviating forecasts and thus to wrong decisions with high losses.