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Is there any way to handle variable-length sequences and train TCN models on them ([the LSTM in PyTorch](https://pytorch.org/docs/stable/generated/torch.nn.utils.rnn.pack_padded_sequence.html) and als…
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Hey! I'm very new to functional data, and have a naive question: can these methods be used to model longitudinal trajectories of discrete states? If not, are they other better methods for clustering t…
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I'd be interested in trying soft-DTW (dynamic time warping) as a loss function. I don't know a whole lot about DTW, but from what I understand, it compares two time series more based on their shape an…
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Reference: http://rdatamining.wordpress.com/2011/08/23/time-series-analysis-and-mining-with-r/
Using this and rpy2, one can do the following:
- DTW to compare different sequences (maybe 2 different d…
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The dtw package in R has some nice ideas for visualizing fits. Would be nice to have similar functionality here:
Toni Giorgino (2009). Computing and Visualizing Dynamic Time Warping Alignments in…
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I received the following question by email*
> Dear Romain
>
> thanks for this toolkit. Can TSlearn handle missing data - quite a big problem in time series analysis of Earth Observation (EO) dat…
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This is the error i am getting while trying to execute the code posted here. Please help me resolve that asap
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Would you have any suggestion regarding a more efficient clustering method?
I asked Mistral AI and was informed about:
Distance-based methods: These methods measure the similarity between time …
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Useful distance measures include but are not limited to:
- [x] (minimum) distance https://shapely.readthedocs.io/en/stable/manual.html#object.distance
- [x] Hausdorff distance https://shapely.read…
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Does anyone happen to come upon any good reference on time series analysis using machine learning techniques?