class tslearn.svm.TimeSeriesSVR(C=1.0, kernel='gak', degree=3, gamma='auto', coef0=0.0, tol=0.001, epsilon=0.1, shrinking=True, cache_size=200, n_jobs=None, verbose=0, max_iter=-1)[source]

Time-series specific Support Vector Regressor.

C : float, optional (default=1.0)

Penalty parameter C of the error term.

kernel : string, optional (default=’gak’)

Specifies the kernel type to be used in the algorithm. It must be one of ‘gak’ or a kernel accepted by sklearn.svm.SVC. If none is given, ‘gak’ will be used. If a callable is given it is used to pre-compute the kernel matrix from data matrices; that matrix should be an array of shape (n_samples, n_samples).

degree : int, optional (default=3)

Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.

gamma : float, optional (default=’auto’)

Kernel coefficient for ‘gak’, ‘rbf’, ‘poly’ and ‘sigmoid’. If gamma is ‘auto’ then:

  • for ‘gak’ kernel, it is computed based on a sampling of the training set (cf tslearn.metrics.gamma_soft_dtw)
  • for other kernels (eg. ‘rbf’), 1/n_features will be used.
coef0 : float, optional (default=0.0)

Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.

tol : float, optional (default=1e-3)

Tolerance for stopping criterion.

epsilon : float, optional (default=0.1)

Epsilon in the epsilon-SVR model. It specifies the epsilon-tube within which no penalty is associated in the training loss function with points predicted within a distance epsilon from the actual value.

shrinking : boolean, optional (default=True)

Whether to use the shrinking heuristic.

cache_size : float, optional (default=200.0)

Specify the size of the kernel cache (in MB).

n_jobs : int or None, optional (default=None)

The number of jobs to run in parallel for GAK cross-similarity matrix computations. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See scikit-learns’ Glossary for more details.

verbose : int, default: 0

Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.

max_iter : int, optional (default=-1)

Hard limit on iterations within solver, or -1 for no limit.

support_ : array-like, shape = [n_SV]

Indices of support vectors.

support_vectors_ : array of shape [n_SV, sz, d]

Support vectors in tslearn dataset format

dual_coef_ : array, shape = [1, n_SV]

Coefficients of the support vector in the decision function.

coef_ : array, shape = [1, n_features]

Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel. coef_ is readonly property derived from dual_coef_ and support_vectors_.

intercept_ : array, shape = [1]

Constants in decision function.

sample_weight : array-like, shape = [n_samples]

Individual weights for each sample

svm_estimator_ : sklearn.svm.SVR

The underlying sklearn estimator


Fast Global Alignment Kernels. Marco Cuturi. ICML 2011.


>>> from tslearn.generators import random_walk_blobs
>>> X, y = random_walk_blobs(n_ts_per_blob=10, sz=64, d=2, n_blobs=2)
>>> import numpy
>>> y = y.astype(numpy.float) + numpy.random.randn(20) * .1
>>> reg = TimeSeriesSVR(kernel="gak", gamma="auto")
>>> reg.fit(X, y).predict(X).shape
>>> sv = reg.support_vectors_
>>> sv.shape  # doctest: +ELLIPSIS
(..., 64, 2)
>>> sv.shape[0] <= 20


fit(X, y[, sample_weight]) Fit the SVM model according to the given training data.
get_params([deep]) Get parameters for this estimator.
predict(X) Predict class for a given set of time series.
score(X, y[, sample_weight]) Return the coefficient of determination \(R^2\) of the prediction.
set_params(**params) Set the parameters of this estimator.
support_vectors_time_series_([X]) DEPRECATED: The use of support_vectors_time_series_ is deprecated in tslearn v0.4 and will be removed in v0.6.
fit(X, y, sample_weight=None)[source]

Fit the SVM model according to the given training data.

X : array-like of shape=(n_ts, sz, d)

Time series dataset.

y : array-like of shape=(n_ts, )

Time series labels.

sample_weight : array-like of shape (n_samples,), default=None

Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.


Get parameters for this estimator.

deep : bool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

params : dict

Parameter names mapped to their values.


Predict class for a given set of time series.

X : array-like of shape=(n_ts, sz, d)

Time series dataset.

array of shape=(n_ts, ) or (n_ts, dim_output), depending on the shape
of the target vector provided at training time.

Predicted targets

score(X, y, sample_weight=None)[source]

Return the coefficient of determination \(R^2\) of the prediction.

The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred) ** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

X : array-like of shape (n_samples, n_features)

Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

y : array-like of shape (n_samples,) or (n_samples, n_outputs)

True values for X.

sample_weight : array-like of shape (n_samples,), default=None

Sample weights.

score : float

\(R^2\) of self.predict(X) wrt. y.


The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).


Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

**params : dict

Estimator parameters.

self : estimator instance

Estimator instance.


DEPRECATED: The use of support_vectors_time_series_ is deprecated in tslearn v0.4 and will be removed in v0.6. Use support_vectors_ property instead.