代码如下所示: steps = [ ("extract",RandomIntervalFeatureExtractor(n_intervals = "sqrt",features=[np.mean, np.std, _slope])),("clf", DecisionTreeClassifier())] time_series_tree = Pipeline(steps) tsf = TimeSeriesForestClassifier(estimator=time_series_tree, n_estimators = 100, criterion = ...
然后将这些特征交给 DecisionTreeClassifier。 代码如下所示: steps = [ ("extract",RandomIntervalFeatureExtractor(n_intervals = "sqrt",features=[np.mean, np.std, _slope])),("clf", DecisionTreeClassifier())] time_series_tree = Pipeline(steps) tsf = TimeSeriesForestClassifier(estimator=time_series...
文中提出监督时间序列森林(Supervised Time Series Forest STSF),基于可判别的区间(discriminatory intervals),机会性地创建用于分类和特征提取的时间序列森林。对于给定的一组长度为 m 的时间序列,可能的区间特征的数量与序列的长度成二次方,结合使用二元搜索策略和特征排名指标(Fisher score),STSF 减少了区间特征空间...
In the testing phase, the trained Ridge classifier outputs probability distributions for all categories in the dataset. The posterior probabilities of each category are then averaged to assign labels with the highest average probability to the input test time series. Until 2019, Ma’s team combined...
We propose a novel TSC algorithm, TS-CHIEF (Time Series Combination of Heterogeneous and Integrated Embedding Forest), which rivals HIVE-COTE in accuracy but requires only a fraction of the runtime. TS-CHIEF constructs an ensemble classifier that integrates the most effective embeddings of time ...
然后将这些特征交给 DecisionTreeClassifier。 代码如下所示: steps = [ ("extract",RandomIntervalFeatureExtractor(n_intervals = "sqrt",features=[np.mean, np.std, _slope])),("clf", DecisionTreeClassifier())] time_series_tree = Pipeline(steps) tsf = TimeSeriesForestClassifier(estimator=time_series...
Time series classification (TSC) is home to a number of algorithm groups that utilise different kinds of discriminatory patterns. One of these groups describes classifiers that predict using phase dependant intervals. The time series forest (TSF) classifier is one of the most well known interval me...
the unknown multivariate time series. Ultra Fast Shapelets (UFS) [24] obtains random shapelets from the multivariate time series and applies a linear SVM or a Random Forest classifier. Subsequently, UFS was enhanced by computing derivatives as features (dUFS) [24]. The ...
This is enabled by the existence of universal properties of bifurcations that are manifested in time series as a dynamical system gets close to a bifurcation7,9. In our previous work, we trained a deep learning classifier to provide an EWS for continuous-time bifurcations, and found it was ...
Data augmentation in deep neural networks is the process of generating artificial data in order to reduce the variance of the classifier with the goal to r... HI Fawaz,G Forestier,J Weber,... 被引量: 8发表: 2018年 TSCF: An Improved Deep Forest Model for Time Series Classification The ...