Sample covariance measurements (using sample data to find out how two populations move with respect to one another) are susceptible to variation over time. Additionally, the accuracy of a covariance matrix decreases as you increase the number of variables without correspondingly increasing the sample size. Thankfully there exist estimation techniques that given sample data will output a more stable population covariance. This notebook goes over how we would set up and use scikit-learn's covariance estimation functions, and why it is beneficial to use them. The main takeaway of this lecture is that when covariance volatility is a concern, it becomes a necessity to use estimation techniques to accurately analyze and forecast data.
A main motivator for this lecture is Ledoit and Wolf's 'Honey I shrunk the covariance matrix paper'.
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