Hello,

I have ran example.py multiple times (200 epoch for training) for the GUN_point data set. I standardized the training/test data, and changed the code make sure it outputs a prediction label of 0 or 1.

But I can only get an accuracy of around 75%. Am I missing anything?

great code thanks only can you clarify if can be your code used for multivariate time series prediction like https://github.com/alan-turing-institute/sktime/blob/master/examples/multivariate_time_series_classification.ipynb

Hi,

When I try to run the algorithm on my timeseries dataset which contains raw numbers, I get the below warning: line 67 in soft_min_layer.py *** RuntimeWarning: invalid value encountered in double_scalars

I believe its because negative exponential of large number is converted to zero when using numpy's exponent method. As I do not prefer to scale the data, is there any other work around ?

I tried to replace numpy's exp with mpmath's exp method, but I still get the same warning.

Great work on this library by the way! I am writing to inquire about how I could best apply shapelet transformation to my use case. If I simply wanted to get the transformed shaplets features on a multivariate time series (cross sectional/panel data), what would I do? What I think I would like to do is to do a shapelet transformation to generate features from a time series to then feed in a keras deep neural network for traditional binary classification. Does this sound advisable to you or would you recommend a different approach? Thanks again, John

hi there,

thanks for sharing the implementation, is it possible to provide the implementation of shapelet clustering in addition to classification?

best,

pandaa

great code thanks may you clarify : will it work for multivariate time series prediction both regression and classification 1 where all values are continues values weight height age target 1 56 160 34 1.2 2 77 170 54 3.5 3 87 167 43 0.7 4 55 198 72 0.5 5 88 176 32 2.3

2 or even will it work for multivariate time series where values are mixture of continues and categorical values for example 2 dimensions have continues values and 3 dimensions are categorical values

color        weight     gender  height  age  target 

1 black 56 m 160 34 yes 2 white 77 f 170 54 no 3 yellow 87 m 167 43 yes 4 white 55 m 198 72 no 5 white 88 f 176 32 yes

I have used your implementation on my own dataset. It seems to work fine when the data is in its original format. However, whenever I normalize or standardize the data, it always predicts only one class (0 in my case), i.e., all the predictions are 0. I cannot seem to understand why is that. Do you have any idea?