pycare检测时间序列异常

1. 获取时间序列数据（官方文档是多特征，本文举例是单特征） 

未安装pycaret的先安装：pip install --pre pycaret （也可以pip install pycaret）

 

 2.用pycaret检测异常一般用pycaret.anomaly 

  

pycaret.anomaly的所有功能：
assign_model,
create_model,
deploy_model,
evaluate_model,
get_config,
get_logs,
load_config,
load_model,
models,
plot_model,
predict_model,
pull,
save_config,
save_model,
set_config,
set_current_experiment,
setup,
tune_model,

from pycaret.anomaly import *
setup = setup(df, session_id = 123)

 

 3.获取pycaret支持的所有检测异常模型

models（）

def create_model(
    model: Union[str, Any],
    fraction: float = 0.05,
    verbose: bool = True,
    fit_kwargs: Optional[dict] = None,
    experiment_custom_tags: Optional[Dict[str, Any]] = None,
    **kwargs,
):

"""
This function trains a given model from the model library. All available
models can be accessed using the ``models`` function.

model: str or scikit-learn compatible object
    ID of an model available in the model library or pass an untrained
    model object consistent with scikit-learn API. Estimators available
    in the model library (ID - Name):

    * 'abod' - Angle-base Outlier Detection
    * 'cluster' - Clustering-Based Local Outlier
    * 'cof' - Connectivity-Based Outlier Factor
    * 'histogram' - Histogram-based Outlier Detection
    * 'knn' - k-Nearest Neighbors Detector
    * 'lof' - Local Outlier Factor
    * 'svm' - One-class SVM detector
    * 'pca' - Princi