My journey through the data science - by Karolina M'Goma

Residuals are the indicator of the model quality. Based on Rob J Hyndman's book "Forecasting: Principles & Practice", residuals in forecasting is difference between observed value and its forecast based on all previous observations. Residuals are useful in checking whether a model has adequately captured the information in the data. All the patterns should be in the model, only randomness remains in the residuals. Therefore the ideal model has to be: uncorrelated has zero mean and useful properties are: constant variance  be normally distributed First I will activate some useful libraries we will be using. library(fpp) library(forecast) For our example I will use dowjones index as a data set. The idea will be to set up already well know simple models like: Mean Model, Naive model and Drift Model. In previous post I described  it more detailed. Next, knowing what attributes  the ideal model should  have we can check which one of those 3 are quite good or  def...