Building on the dataset presented in the previous chapter, this chapter explores using historical information (as represented by previous DBpedia versions) to perform feature engineering using historical features.Working with historical data makes all Feature Engineering complex but the whole concept of “truth,” the immutablity of the target class is challenged. Great feature for a particular class have to become acceptable features for a different class. Topics covered include imputing timestamped data, lagged features and moving window averaging of the data. Due to unavailability of population data for cities, a second dataset revolving around countries is introduced to perform population prediction using time series ARIMA models over 50 years of data, as provided by the world bank. The chapter exemplifies different methods to blend machine learning with time series models, including using their output as another feature or training a model to predict their errors.