Calculations of the most common metrics of automated advertisement and plotting of them with trend and forecast. Calculations and description of metrics is taken from different RTB platforms support documentation. Plotting and forecasting is based on packages 'forecast', described in Rob J Hyndman and George Athanasopoulos (2021) "Forecasting: Principles and Practice" <https://otexts.com/fpp3/> and Rob J Hyndman et al "Documentation for 'forecast'" (2003) <https://pkg.robjhyndman.com/forecast/>, and 'ggplot2', described in Hadley Wickham et al "Documentation for 'ggplot2'" (2015) <https://ggplot2.tidyverse.org/>, and Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen (2015) "ggplot2: Elegant Graphics for Data Analysis" <https://ggplot2-book.org/>.

A framework for automated machine learning. Concretely, the focus is on the optimisation of bagging workflows. A bagging workflows is composed by three phases: (i) generation: which and how many predictive models to learn; (ii) pruning: after learning a set of models, the worst ones are cut off from the ensemble; and (iii) integration: how the models are combined for predicting a new observation. autoBagging optimises these processes by combining metalearning and a learning to rank approach to learn from metadata. It automatically ranks 63 bagging workflows by exploiting past performance and dataset characterization. A complete description of the method can be found in: Pinto, F., Cerqueira, V., Soares, C., Mendes-Moreira, J. (2017): "autoBagging: Learning to Rank Bagging Workflows with Metalearning" arXiv preprint arXiv:1706.09367.

A modified version of the classification and regression tree (CART) 
    algorithm for modelling spatial data that features coordinate information.
    Coordinate information can be used to evaluate measures of spatial
    autocorrelation and spatial compactness during the splitting phase of the
    tree, leading to better predictions and more physically realistic predictions
    on these types of datasets. These methods are described in Ancell and Bean (2021)
    <arXiv:2101.08258>.