Pre-processing#

A common operation on time series is to pre-processes each series individually, e.g., normalizing each timestep or truncating multivariate time series to have uniform length, the datasets.preprocess-module implements a selection of common operations that are performed along the time dimension (i.e., along the last dimension of the time series array). Currently, Wildboar supports the following operations:

standardize: Standardize each time step to have zero mean and unit variance.
minmax_scale: Normalize each time step in a predefined range, by default between 0 and 1.
maxabs_scale: Scale each time step by the maximum absolute value,
truncate: Truncate each time series to have uniform length, i.e., to the length of the shortest time series.

In contrast to feature-wise preprocessing, the preprocessing operations in the wildboar.datasets-module operate sample-wise and are state-less, i.e., we can reuse them for both the training and testing parts of our data. To simplify the application of preprocessing, the wildboar.datasets.load_dataset function accepts a preprocess parameter:

from wildboar.datasets import load_dataset
from wildboar.datasets import preprocess
x, y = load_dataset("GunPoint", preprocess=preprocess.minmax_scale)

The preprocess accept both a function that expects a ndarray and returns a new preprocessed ndarray or a named preprocessor as a string. The names are the same as the function names enumerated above. For example, the previous code snippet could be rewritten as:

x, y = load_dataset("GunPoint", preprocess="minmax_scale")

A crude way of dealing with time series of unequal length is to truncate longer time series to the length of the shortest time series. In Wildboar, we can use truncate to accomplish this.

In Wildboar, all time series datasets are traditional Numpy-arrays with a specified shape, i.e., (n_samples, n_dims, n_timesteps). To support time series of unequal length, we use a specific value to denote end-of-sequence (EOS). We can get the EOS value from wildboar.eos, and use wildboar.iseos to check for this value, and get the length of each series:

import wildboar
length = wildboar.iseos(x).argmax(axis=-1)

For example, we could use the following code to plot the length of each dimension of a multivariate time series:

>>> import matplotlib.pyplot as plt
>>> eos = wildboar.iseos(x).argmax(axis=-1)
>>> fig, ax = plt.subplots(nrows=3) # assuming 3 dimensions
>>> for dim in range(eos.shape[1]):
...    eos[eos[:, dim] == 0] = x.shape[-1]  # if eos == n_timestep
...    ax[dim].scatter(np.arange(eos.shape[0]), eos[:, dim], marker="x")
...    ax[dim].set_ylabel(f"dim {dim}")

Running the code with a dataset (e.g., SpokenArabicDigits from wildboar/ucrmts) would yield a figure similar to this.

Truncating the time series to the shortest dimension (in the example this is 26 time steps), using preprocess.truncate(x), results in a figure similar to this:

Since many algorithms in Wildboar only support dimensions and samples of uniform length, we can preprocess the time series using the truncate function. One should note that truncating is very crude and result in data loss.

Pre-processing#

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