Hands-On Unsupervised Learning with Python

Diagnostic analysis

Till now, we have worked with output data, which has been observed after a specific underlying process has generated it. The natural question after having described the system relates to the causes. Temperature depends on many meteorological and geographical factors, which can be either easily observable or completely hidden. Seasonality in the time series is clearly influenced by the period of the year, but what about the outliers?

For example, we have discovered a peak in a region identified as winter. How can we justify it? In a simplistic approach, this can be considered as a noisy outlier that can be filtered out. However, if it has been observed and there's a ground truth behind the measure (for example, all the ...