Time Series and Forecasting
Update Botie's demand estimate
BOTIE
Examples, experiments and workspace controls
Connect a tool's inputs to see what it produces. You can try a branch before connecting it to Result.
Try your own inputs
Use a copy of the example to try your own data. Keep the problem's assumptions in mind, and predict the effect of a change before checking the output. You can also add another branch to compare two ways of doing the calculation.
Context for this challenge
Subtract the old level from the reading, multiply by alpha, and add the correction to the old level.
A new demand observation has arrived. Botie can revise the retained level without reconstructing the full history.
Find the new observation's difference from the old level, scale that surprise by alpha, and add it back.
Examples and results
- Previous level estimate
10
- New observed demand
14
- Update fraction
0.25
- Required result
11
- Previous level estimate
8
- New observed demand
20
- Update fraction
0
- Required result
8
Try a fresh construction
This clears this draft, including its undo history. Earlier successful attempts stay recorded. Build again without opening hints to record an independent attempt.
Stuck? Inspect a worked construction
The tools below form one possible solution. You can assemble it, follow its signals, and undo that change to return to your own construction.
Assumptions and test precision
This is one level update with a supplied alpha in [0,1]. It does not fit alpha or model trend and seasonality. Numerical tests allow a difference of 0.001. Lists and structures must also have the right shape.
A quieter reading lowers the current level
This check uses the pieces from “Correct the retained demand level”.
BOTIE
- Previous level estimate
30
- New observed demand
10
- Update fraction
0.4
Use a number, text, or an array matching the requested result. For a matrix, put each row in brackets. Saving your prediction runs your current construction on these inputs and checks the earlier examples too.
Build a construction in the workspace above first. This case will test those pieces.
How would lowering alpha alter the response to a single unusual day?
The update fraction sets responsiveness; choosing it well requires evaluation beyond one update.
This is one level update with a supplied alpha in [0,1]. It does not fit alpha or model trend and seasonality.
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