可变长度时间序列的方法¶
本页面列出了tslearn中能够处理包含不同长度时间序列的数据集的机器学习方法。我们还提供了使用以下变长时间序列数据集的这些方法的示例用法：
from tslearn.utils import to_time_series_dataset
X = to_time_series_dataset([[1, 2, 3, 4], [1, 2, 3], [2, 5, 6, 7, 8, 9]])
y = [0, 0, 1]
分类¶
示例¶
from tslearn.neighbors import KNeighborsTimeSeriesClassifier
knn = KNeighborsTimeSeriesClassifier(n_neighbors=2)
knn.fit(X, y)
from tslearn.svm import TimeSeriesSVC
clf = TimeSeriesSVC(C=1.0, kernel="gak")
clf.fit(X, y)
from tslearn.shapelets import LearningShapelets
clf = LearningShapelets(n_shapelets_per_size={3: 1})
clf.fit(X, y)
回归¶
示例¶
from tslearn.svm import TimeSeriesSVR
clf = TimeSeriesSVR(C=1.0, kernel="gak")
y_reg = [1.3, 5.2, -12.2]
clf.fit(X, y_reg)
最近邻搜索¶
示例¶
from tslearn.neighbors import KNeighborsTimeSeries
knn = KNeighborsTimeSeries(n_neighbors=2)
knn.fit(X)
knn.kneighbors() # Search for neighbors using series from `X` as queries
knn.kneighbors(X2) # Search for neighbors using series from `X2` as queries
聚类¶
示例¶
from tslearn.clustering import KernelKMeans
gak_km = KernelKMeans(n_clusters=2, kernel="gak")
labels_gak = gak_km.fit_predict(X)
from tslearn.clustering import TimeSeriesKMeans
km = TimeSeriesKMeans(n_clusters=2, metric="dtw")
labels = km.fit_predict(X)
km_bis = TimeSeriesKMeans(n_clusters=2, metric="softdtw")
labels_bis = km_bis.fit_predict(X)
from tslearn.clustering import TimeSeriesKMeans, silhouette_score
km = TimeSeriesKMeans(n_clusters=2, metric="dtw")
labels = km.fit_predict(X)
silhouette_score(X, labels, metric="dtw")
重心计算¶
示例¶
from tslearn.barycenters import dtw_barycenter_averaging
bar = dtw_barycenter_averaging(X, barycenter_size=3)
from tslearn.barycenters import softdtw_barycenter
from tslearn.utils import ts_zeros
initial_barycenter = ts_zeros(sz=5)
bar = softdtw_barycenter(X, init=initial_barycenter)
模型选择¶
此外，scikit-learn提供的模型选择工具可以以标准方式用于可变长度数据，例如：
from sklearn.model_selection import KFold, GridSearchCV
from tslearn.neighbors import KNeighborsTimeSeriesClassifier
knn = KNeighborsTimeSeriesClassifier(metric="dtw")
p_grid = {"n_neighbors": [1, 5]}
cv = KFold(n_splits=2, shuffle=True, random_state=0)
clf = GridSearchCV(estimator=knn, param_grid=p_grid, cv=cv)
clf.fit(X, y)
重采样¶
最后，如果你想使用一种不能在可变长度时间序列上运行的方法，一个选择是首先对你的数据进行重采样，使所有时间序列具有相同的长度，然后在这个重采样版本的数据集上运行你的方法。
但请注意，重采样会在您的数据中引入时间上的失真。请谨慎使用！
from tslearn.preprocessing import TimeSeriesResampler
resampled_X = TimeSeriesResampler(sz=X.shape[1]).fit_transform(X)