tslearn.generators.random_walk_blobs¶
- tslearn.generators.random_walk_blobs(n_ts_per_blob=100, sz=256, d=1, n_blobs=2, noise_level=1.0, random_state=None)[source]¶
Blob-based random walk time series generator.
Generate n_ts_per_blobs * n_blobs time series of size sz and dimensionality d. Generated time series follow the model:
\[ts[t] = ts[t - 1] + a\]where \(a\) is drawn from a normal distribution of mean mu and standard deviation std.
Each blob contains time series derived from a same seed time series with added white noise.
- Parameters:
- n_ts_per_blobint (default: 100)
Number of time series in each blob
- szint (default: 256)
Length of time series (number of time instants)
- dint (default: 1)
Dimensionality of time series
- n_blobsint (default: 2)
Number of blobs
- noise_levelfloat (default: 1.)
Standard deviation of white noise added to time series in each blob
- random_stateinteger or numpy.RandomState or None (default: None)
Generator used to draw the time series. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.
- Returns:
- numpy.ndarray
A dataset of random walk time series
- numpy.ndarray
Labels associated to random walk time series (blob id)
Examples
>>> X, y = random_walk_blobs(n_ts_per_blob=100, sz=256, d=5, n_blobs=3) >>> X.shape (300, 256, 5) >>> y.shape (300,)
