Released Notes

Version 0.1.0

HyperTS is an AutoML and AutoDL tool which focuses on processing time series datasets. It supports the following features:

  • Support the following data and tasks

    • Forecasting, classification, and regression

    • Univariate, multivariates, covariates

  • Data cleaning:

    • Repeated columns cleaning

    • Columns types correction

    • ID column cleaning

    • Constant covariate columns cleaning

    • Deleting covariate columns with missing values

    • Deleting samples without targets

  • Data preprocessing:

    • TimeStamp impution

    • Missing value simple impution

    • Missing value average moving impution

    • Outliers processing

    • OrdinalEncoder

    • LabelEncoder

    • StandardScaler

    • MinMaxScaler

    • MaxAbsScaler

    • Log(x+1)

  • Data split:

    • Split training dataset and test dataset in the order of time sequence

  • Dataset creation

    • Create batches of inputs and targets by sliding window

  • Model & mode:

    • Statistical models: Prophet、ARIMA、VAR、TSForest、KNeighbors

    • Deep learning models: DeepAR、RNN、GRU、LSTM、LSTNet

  • Evaluation method:

    • Train-Validation-Holdout

  • Visualization:

    • Human interactive plot

    • Plot options: historical data, forecasting data and actural data

    • Confidence intervals

Version 0.1.2.1

Details of the HypertTS update are as follows:

  • Supports cross validation.

  • Supports greedy ensemble.

  • Supports time series forecasting data without timestamp column.

  • Supports time series forecasting truncation training set to train.

  • Supports time series forecasting of discrete data (no fixed time frequency).

  • Supports Fourier inference period.

  • Supports non-invasive parameters tuning.

  • Optimizes search space and architecture.

  • Fixes some bugs.

Version 0.1.3

Details of the HypertTS update are as follows:

  • Tuning search space hyperparameters;

  • Added report_best_trial_params;

  • Fixed ARIMA to be compatible with version 0.12.1 and above;

  • Fixed the pt issue of LSTNet;

  • Simplified custom search space, task, timestamp, covariables and metircs can not be passed;

  • Added OutliersTransformer, supported dynamic handling of outliers;

  • Adjusted final train processing - lr, batch_size, epcochs and so on;

  • Added time series meta-feature extractor;

  • Added Time2Vec, RevIN, etc. layers;

  • Added N-Beats time series forecasting model;

  • Added InceptionTime time series classification model;

  • Supported dynamic downsampling for time series forecasting;

  • Refactored positive label inference method;

  • Added neural architecture search mode;

  • Fixed some known issues.

Version 0.1.4

See Version 0.1.3.

Version 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.