Featurizing Time Series

Functions used to transform time series into a dataframe that can be used to create tabular dataloaders.

In this case we are using tsfresh that is one of the most widely known libraries used to create features from time series. You can get more details about this library here: https://tsfresh.readthedocs.io/en/latest/

source

get_ts_features

 get_ts_features (X:Union[numpy.ndarray,torch.Tensor],
                  y:Union[NoneType,numpy.ndarray,torch.Tensor]=None,
                  features:Union[str,dict]='min',
                  n_jobs:Optional[int]=None, **kwargs)

Args: X: np.array or torch.Tesnor of shape [samples, dimensions, timesteps]. y: Not required for unlabeled data. Otherwise, you need to pass it. features: ‘min’, ‘efficient’, ‘all’, or a dictionary. Be aware that ‘efficient’ and ‘all’ may required substantial memory and time.

dsid = 'NATOPS'
X, y, splits = get_UCR_data(dsid, return_split=False)
X.shape
(360, 24, 51)

There are 3 levels of fatures you can extract: ‘min’, ‘efficient’ and ‘all’. I’d encourage you to start with min as feature creation may take a long time.

In addition to this, you can pass a dictionary to build the desired features (see tsfresh documentation in the link above).

ts_features_df = get_ts_features(X, y)
ts_features_df.shape
Feature Extraction: 100%|██████████| 40/40 [00:09<00:00,  4.00it/s]
(360, 241)

The ‘min’ set creates a dataframe with 8 features per channel + 1 per target (total 193) for each time series sample (360).

cont_names = ts_features_df.columns[:-1]
y_names = 'target'
dls = get_tabular_dls(ts_features_df, splits=splits, cont_names=cont_names, y_names=y_names)
dls.show_batch()
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x_cat, x_cont, yb = first(dls.train)
x_cont[:10]
tensor([[-0.8581, -0.1346, -0.8581,  ...,  0.3235,  0.5841, -0.6917],
        [-0.0406,  0.0344, -0.0406,  ..., -0.0171, -1.7253,  1.2745],
        [ 1.7966, -0.0497,  1.7966,  ..., -0.6516,  0.9802, -1.0290],
        ...,
        [ 0.2804,  0.4747,  0.2804,  ..., -0.4816, -0.3325,  0.0887],
        [-0.1472, -0.1884, -0.1472,  ..., -0.1036, -1.2258,  0.8491],
        [-0.9960, -0.6116, -0.9960,  ...,  0.4602,  3.3105, -3.0129]])