As per Ariel Nunez's suggestion, for bucketed time series data like the daily mailing list activity counts, there should be machinery for doing a component analysis of the activity.
This means turning a time stamp into a feature vector. The featurization frameowrk should be flexible enough to support both periodical features (such as 'Mondays'), progression over time (i.e. days from origin), and landmark events (such as release dates).
Use PCA or a similar algorithm to get a sense of the contribution of each feature to the total activity. Document this process in an I Python notebook, and include comments on the limitations of the linear model.
As per Ariel Nunez's suggestion, for bucketed time series data like the daily mailing list activity counts, there should be machinery for doing a component analysis of the activity.
This means turning a time stamp into a feature vector. The featurization frameowrk should be flexible enough to support both periodical features (such as 'Mondays'), progression over time (i.e. days from origin), and landmark events (such as release dates).
Use PCA or a similar algorithm to get a sense of the contribution of each feature to the total activity. Document this process in an I Python notebook, and include comments on the limitations of the linear model.