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Expose Markov Transition Matrix from pyts.image.MarkovTransitionField. #120
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If there would be a single Markov transition matrix for the whole training set of time series, then it would be computed in the However, a Markov transition matrix is computed for every time series in the training set, because the bin edges and the transition probabilities are independently computed using the values of each time series. This is what is proposed in the paper introducing this method and thus what is implemented in this package. Did you think that a single Markov transition matrix was computed for the whole training set? It's not the first time that this issue / question is reported / suggested. Maybe I should highlight this point in the documentation and possibly enable the other behavior (computing a single Markov transition matrix for the whole training set). |
Hi, sry for a very late reply. I understand ur logic that if the matrices are computed once for any input time series, then theres no need to "fit" them as a pre-step. If all of these doesn't make sense, would it still be possible to expose all of the underlaying GMF/MTF matrices? |
The Since a lot of transformers in pyts apply the transformation on each time series (i.e., row) independently instead of feature (i.e., column) independently, the Moreover, I think that limiting the
For me, it would only make sense if the estimator would learn a single Markov Transition Matrix for all the time series when calling
I'm not sure to understand what you mean. The |
Sorry again for a very late reply. Its indeed a bit tricky with the whole "fit, transform" scenario. My OP was not coming from the ML fields actually, more like probability analysis.
So yeah, what I really wanted was more like functions to compute the intermediate data objects rather than to expose them during the transformation. Although this topic could also belong to a separate feature request instead, or what do you think? |
The Markov Transition Matrix contained in the MTF transformer could be useful in many cases.
It shouldnt be too much work to expose it (as well as the quantile boundaries) by storing it in the transformer once its fitted. This also means that the computation of the Markov Transition Matrix should be done in the fit pass instead of each time the transform is called. i.e. move:
in pyts/pyts/image/mtf.py
into
Is it possible to implement this?
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