Feedback no. 4
In the current forecasting notebook, we assumed that the maximum number of days of data that we are guaranteed to have at runtime is 6. However after talking to ceph subject matter experts, it seems that there might be some flexibility there.
On the one hand, having more amount of data available might improve model accuracy. But on the other hand, this would mean users have to store more health data locally. The main purpose of this issue is to figure out the “sweet spot” such that not a lot of data is stored and yet model performance is also improved.
As a data scientist, I want to explore how model performance changes with number of days of data available at runtime, to find a reasonable compromise between amount of data stored and model accuracy achieved.
Acceptance criteria:
Feedback no. 4
In the current forecasting notebook, we assumed that the maximum number of days of data that we are guaranteed to have at runtime is 6. However after talking to ceph subject matter experts, it seems that there might be some flexibility there.
On the one hand, having more amount of data available might improve model accuracy. But on the other hand, this would mean users have to store more health data locally. The main purpose of this issue is to figure out the “sweet spot” such that not a lot of data is stored and yet model performance is also improved.
As a data scientist, I want to explore how model performance changes with number of days of data available at runtime, to find a reasonable compromise between amount of data stored and model accuracy achieved.
Acceptance criteria: