Release 0.2.0

Details of the HypertTS update are as follows:

• Supported time series anomaly detection task, and adapt to the full pipeline automation process;

• Added IForest anomaly detection model (stats mode);

• Added TSOneClassSVM anomaly detection model(stats mode);

• Added ConvVAE anomaly detection model(dl mode);

• Added realKnownCause anomaly detection dataset;

• Supported the visualization of anomaly detection results, and can analyze the anomaly location and severity;

• Compatible with Prophet version 1.1.1, now pip install hyperts for simultaneous successful prophet installation;

• Compatible with all versions of scipy;

• Added API documentation module;

• Supported for model persistence (saving and reloading trained models);

• In ```model.predict()``, fixed missing value handling;

• For the time series forecast task, the forecast function of DL model is calibrated;

• DLClassRegressSearchSpace was refactored for better adaptation to regression task;

• Extend InceptionTime to solve the regression task;

• Fixed some known issues;

• Thanks to @peter Cotton for his contributions to hyperts.