All Questions
7 questions
5
votes
0
answers
690
views
Predict churn in a range of time after observation window is finished
I'm building a churn model. Each user's historic data (observation window) is a constant period, but each observation window contains different dates.
For example the next figure:
Let's say, that the ...
- 77
1
vote
0
answers
350
views
what is it an activity window in churn model?
I know that in a churn model many times you define an observation window (historic data) and a performance window (also dependent window, or response window).
I have read an article that the authors (...
- 77
1
vote
0
answers
356
views
Rolling forecast vs. static training data for financial timeseries?
I want to train a statistical model to predict financial asset returns.
I'm wondering whether it would be more effective to train a rolling forecast model rather than training a single model with a ...
- 1,334
2
votes
1
answer
360
views
Discovering peaks/patterns in time-series and clustering them
I have a dataset which contains minute level sensor measurements. Sample is shown here:
To me useful information are these peaks in time series, mostly their peak and duration. My idea is to take out ...
- 21
3
votes
1
answer
3k
views
Is moving average(sliding window) a smoothing technique or forecasting technique?
The rolling average method is mostly used to produce a smoothed series by removing noise. For ex- 3 window moving average, in general practice, the output for the fourth period is the 3 window moving ...
- 80
2
votes
1
answer
68
views
Ranking with multiple weights/ features
We have entities where every entity has start ($s_i$) and end ($e_i$) times and count $c_i$.
An entity is important if its interval ($e_i - s_i$) is large and if its $c_i$ is large.
Here's what I ...
- 175
1
vote
0
answers
47
views
Classify time series with unequal lenghts [closed]
I have a set of time series sensor measurements (acceleration and gyroscope readings) for driving events (harsh acceleration, harsh brake …) with the type, start and end of each event.
I need to ...
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