Multi yoy no label

[cdeline]

• Code changes are covered by tests
• Code changes have been evaluated for compatibility/integration with TrendAnalysis
• New functions added to __init__.py
• API.rst is up to date, along with other sphinx docs pages
• Example notebooks are rerun and differences in results scrutinized
• Updated changelog
• Update degradation and multi-YoY notebook example to remove label=
• Remove old degradation_timeseries_plot_old function cruft

[cdeline]

OK, so it looks like taking the median of every slope whose center-point falls within the moving window will re-create the original pd.rolling() approach for the standard (non-multi-yoy) case. Another change is that the new approach effectively labels the datapoints with label=center while the old approach is defaulting to label=right. This could be fixed if we wanted to go back to the original implementation.

[cdeline]

.. But with the multi-YoY output, taking the median of all slopes rather than a pointwise average will result in much more smoothing in the center of the timeseries (where you have more slopes available), but more variability at start and end of the timeseries when you're only averaging the 1-yr slopes.

[martin-springer]

.. But with the multi-YoY output, taking the median of all slopes rather than a pointwise average will result in much more smoothing in the center of the timeseries (where you have more slopes available), but more variability at start and end of the timeseries when you're only averaging the 1-yr slopes.

Agreed, with the multi-yoy approach the time series plot becomes less meaningful as it kind of converges to the overall system degradation rate in the center of the plot and that's kind of unrelated to the years shown in the x-labels. How about, for plotting purposes, we only use the 1-year slopes (slopes <1.5 year to account for leap years) for the whole plot? Would this give us a better representation of the system degradation along the time axis?

[cdeline]

We could leverage the rolling_days input parameter - like filter out slopes that are longer than > (rolling_days *1.1). Then you should get similar plots whether you use multi-yoy or regular.