anchor rules for time series classification

[JaviL]

thanks for the excellent job.

I have created a dataset where each target variable has a 2D array. The dataset format is the one used by TSAI:

Considerations about our 3D input data or X vs a 2D target vars Y (my dataset are just numpy arrays)

To be able to use timeseriesAI your data needs to have 3 dimensions:

• number of samples
• number of features (aka variables, dimensions, channels)
• number of steps (or length, time steps, sequence steps)
  (from this noteboook)

here a diagram of the dataset format

The problem is not only related with time series classification, but with any tabular data, where each target variable contains multiple
values per input feature var.

The question is:

could I use this package to explain anchor rules of a model trained with this format of data?

here a code example - 01_Intro_to_Time_Series_Classification.ipynb I am using to generate my own model

PD: This format would be quite similar to a B/W image classification dataset, where we have one image per sample and the labels are just numbers (NMIST example)

[JaviL]

Yes. That's what I thought

[Vidushi Jain]

Hi,
Please let me know if you were able to use it on time series classification.
Thank you so much.