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README.md

Resample data based on DTW alignment

Purpose

The goal of this code is to allow irregular resampling of timestamped data. We assume we have a variable that informs about how advanced we are in the process for each timestamp in a time series. This variable is called base modality in the following. This variable could be, for example, the amount of discharge if we study water quality during a flood (i.e. in this case, aligning discharge corresponds to aligning floods in a plausible way).

We will then use this variable to align other modalities of our dataset. To do so, we record DTW path obtained when aligning base modality of each time series with that of a reference time series. This path is then used to perform irregular resampling of time series in our dataset w.r.t. alignment of base modalities.

We refer the interested reader to the following publication for more details:

@article{dupas:halshs-01228397,
  TITLE = {{Identifying seasonal patterns of phosphorus storm dynamics with dynamic time warping}},
  AUTHOR = {Dupas, R{\'e}mi and Tavenard, Romain and Fovet, Oph{\'e}lie and Gilliet, Nicolas and Grimaldi, Catherine and Gascuel-Odoux, Chantal},
  JOURNAL = {{Water Resources Research}},
  PUBLISHER = {{American Geophysical Union}},
  VOLUME = {51},
  NUMBER = {11},
  PAGES = {8868--8882},
  YEAR = {2015},
  DOI = {10.1002/2015WR017338},
  PDF = {https://halshs.archives-ouvertes.fr/halshs-01228397/file/article_WRR_accepte_avec_fig.pdf}
}

Also, if you use our code in a scientific publication, it would be nice to cite us using the above-mentionned reference :)

Code details

Example tests are provided in files test_sampling.py and test_clustering.py or, as notebooks, in sampling.ipynb and clustering.ipynb. In a few words, data should be resampled using the class DTWSampler that is supposed to be a standard sklearn transformer. Hence, fitting the sampler can be performed via:

from sampler import DTWSampler

s = DTWSampler(scaling_col_idx=0, reference_idx=0, d=d)
s.fit(data)

Here, data is a 2-dimensional array of shape (n_ts, l * d) where n_ts is the number of time series in the dataset, l is the length of a time series and d is the number of modalities provided for each time-stamp (including base modality). Basically, if you have your data stored in a 3-dimensional array data_3d of shape (n_ts, l, d), you should just do:

data = data_3d.reshape((data_3d.shape[0], -1))

scaling_col_idx is the index of the base modality and reference_idx is the index of the time series to be used as reference.

And applying the transformation to some new data is done via:

transformed_data = s.transform(newdata)

If one wants to do both fit and transform on the same data, the following should do it:

transformed_data = s.fit_transform(data)

Note that, in order to comply with sklearn standards, transformed_data is a 2d-array. If you want to get back to your (n_ts, n_samples, d) shape, just use:

transformed_data = s.fit_transform(data).reshape((data.shape[0], -1, s.d))

save_path=True option

We have added an option save_path to DTWSampler. This one should be used with great care! It correspond to cases for which the data fed to the model is composed of a single reference time series at index reference_idx and all other time series have the same time shift with respect to the reference, such that it is sufficient to compute a single DTW instead of one DTW per time series.

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