Forecasting Methods

Summary Overview

There are several different methods we can use for forecasting each with its own pros/cons.

New cases

Method	Pros	Cons
New cases (raw)	High granularity: In theory tell public officials exactly what days diagnosis will occur	<>
New cases 7 day rolling average	Removes the noise associated with reporting delays. Easier to see if model is learning disease trajectories	Hard to forecast for non-stationary data and predict out of distribution events
New cases difference	Still high granularity Works better on non-stationary data
7 day rolling average difference	Removes the noise associated with reporting delays. Helps solve non-stationary data problems	Less intuitive and comparable to other models

Hospitalizations

Method	Pros	Cons
New hospitalizations (raw)	High granularity: In theory tell public officials exactly what days diagnosis will occur	<>
Total active hospitalizations	Removes the noise associated with reporting delays. Easier to see if model is learning disease trajectories	Hard to forecast for non-stationary data and predict out of distribution events
Hospitalizations difference	Still high granularity Works better on non-stationary data

Analysis