Comments (2)
Consider what is done with e.g. Lorentz attractors. Since it is a chaotic system, we can't expect to make exact predictions, but taken over time the "patterns" should be similar.
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Possible idea: annealing/time-exponential smoothing. If a pixel is "hot" at a time close to the true time, it should count more than if it happens at a totally different time.
Simultaneously create "heat map" of the true data, then subtract.
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Related Issues (20)
- Keep/calculate shapes in input maps, use in debug/visualization HOT 1
- Efficient G-weighted least squares HOT 1
- do some actual anomaly detection on kuroshio!
- set a seed to get determinism HOT 1
- Feature request: Input map sequence, or rescaling parameter HOT 3
- write_video is broken somehow
- Add time-component to IMED HOT 2
- Add convolutional distance/loss function HOT 1
- Recreate Fig. 5.13 of thesis automatically HOT 3
- Publish package on PyPy
- `TorchImageDataset` broken
- Fix tests... HOT 1
- Use online least squares for sliding window optimization HOT 1
- Make length scale in IMED adjustable HOT 1
- Store git hash and command line in all generated .nc-files
- Use recursive linear least squares for online ESN HOT 1
- Add Bohrium support HOT 3
- Don't predict the mask
- Add timing of different parts HOT 1
- create seperate pred-perf script for with/without comparison to cycle/lstm prediction
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