Statistics | Deep Learning | Neural Architecture Search

:dizzy: Easy-to-use, powerful, and unified full pipeline automated time series toolkit. Supports forecasting, classification, regression, and anomaly detection. ## We Are Hiring！ Dear folks, we are offering challenging opportunities located in Beijing for both professionals and students who are keen on AutoML/NAS. Come be a part of DataCanvas! Please send your CV to yangjian@zetyun.com. (Application deadline: TBD.) ## Overview HyperTS is a Python package that provides an end-to-end time series (TS) analysis toolkit. It covers complete and flexible AutoML workflows for TS, including data clearning, preprocessing, feature engineering, model selection, hyperparamter optimization, result evaluation, and visualization. Multi-mode drive, light-heavy combination is the highlighted features of HyperTS. Therefore, statistical models (STATS), deep learning (DL), and neural architecture search (NAS) can be switched arbitrarily to get a powerful TS estimator. As an easy-to-use and lower-threshold API, users can get a model after simply running the experiment, and then execute ```.predict()```, ```.predict_proba()```, ```.evalute()```, ```.plot()``` for various time series analysis. ## Installation Note: - Prophet is required by HyperTS, install it from ``conda`` before installing HyperTS using ``pip``. - Tensorflow is an optional dependency for HyperTS, install it if using DL and NAS mode. HyperTS is available on Pypi and can be installed with ``pip``: ```bash pip install hyperts ``` You can also install HyperTS from ``conda`` via the ``conda-forge`` channel: ```bash conda install -c conda-forge hyperts ``` If you would like the most up-to-date version, you can instead install direclty from ``github``: ```bash git clone git@github.com:DataCanvasIO/HyperTS.git cd HyperTS pip install -e . pip install tensorflow #optional, recommended version: >=2.0.0,<=2.10.0 ```` For more installation tips, see [installation](https://hyperts.readthedocs.io/en/latest/contents/0200_installation.html). ## Tutorial |[English Docs](https://hyperts.readthedocs.io/en/latest/) / [Chinese Docs](https://hyperts.readthedocs.io/zh_CN/latest)| Discription | | --------------------------------- | --------------------------------- | [Expected Data Format](https://hyperts.readthedocs.io/en/latest/contents/0300_dataformat.html)|What data formats do HyperTS expect?| |[Quick Start](https://hyperts.readthedocs.io/en/latest/contents/0400_quick_start.html)| How to get started quickly with HyperTS?| |[Advanced Ladder](https://hyperts.readthedocs.io/en/latest/contents/0500_advanced_config.html)|How to realize the potential of HyperTS?| |[Custom Functions](https://hyperts.readthedocs.io/en/latest/contents/0600_custom_functions.html)|How to customize the functions of HyperTS?| ## Examples Time Series Forecasting Users can quickly create and ```run()``` an experiment with ```make_experiment()```, where ```train_data```, and ```task``` are required input parameters. In the following forecast example, we define the experiment as a multivariate-forecast ```task```, and use the statistical model (stat ```mode```) . Besides, the mandatory arguments ```timestamp``` and ```covariates``` (if have) should also be defined in the experiment. ```python from hyperts import make_experiment from hyperts.datasets import load_network_traffic from sklearn.model_selection import train_test_split data = load_network_traffic() train_data, test_data = train_test_split(data, test_size=0.2, shuffle=False) model = make_experiment(train_data.copy(), task='multivariate-forecast', mode='stats', timestamp='TimeStamp', covariates=['HourSin', 'WeekCos', 'CBWD']).run() X_test, y_test = model.split_X_y(test_data.copy()) y_pred = model.predict(X_test) scores = model.evaluate(y_test, y_pred) model.plot(forecast=y_pred, actual=test_data) ``` 

Time Series Classification (click to expand)

```python from hyperts import make_experiment from hyperts.datasets import load_basic_motions from sklearn.metrics import f1_score from sklearn.model_selection import train_test_split data = load_basic_motions() train_data, test_data = train_test_split(data, test_size=0.2) model = make_experiment(train_data.copy(), task='classification', mode='dl', tf_gpu_usage_strategy=1, reward_metric='accuracy', max_trials=30, early_stopping_rounds=10).run() X_test, y_test = model.split_X_y(test_data.copy()) y_pred = model.predict(X_test) y_proba = model.predict_proba(X_test) scores = model.evaluate(y_test, y_pred, y_proba=y_proba, metrics=['accuracy', 'auc', f1_score]) print(scores) ```

Time Series Anomaly Detection (click to expand)

```python from hyperts import make_experiment from hyperts.datasets import load_real_known_cause_dataset from sklearn.model_selection import train_test_split data = load_real_known_cause_dataset() ground_truth = data.pop('anomaly') detection_length = 15000 train_data, test_data = train_test_split(data, test_size=detection_length, shuffle=False) model = make_experiment(train_data.copy(), task='detection', mode='stats', reward_metric='f1', max_trials=30, early_stopping_rounds=10).run() X_test, _ = model.split_X_y(test_data.copy()) y_test = ground_truth.iloc[-detection_length:] y_pred = model.predict(X_test) y_proba = model.predict_proba(X_test) scores = model.evaluate(y_test, y_pred, y_proba=y_proba) model.plot(y_pred, actual=test_data, history=train_data, interactive=False) ```

Time Series MetaFeatures Extract (click to expand)

```python from hyperts.toolbox import metafeatures_from_timeseries from hyperts.datasets import load_random_univariate_forecast_dataset data = load_random_univariate_forecast_dataset() metafeatures = metafeatures_from_timeseries(x=data, timestamp='ds', scale_ts=True) ```

**More detailed guides:** [EXAMPLES.](https://github.com/DataCanvasIO/HyperTS/tree/main/examples) ## Key Features HyperTS supports the following features: **Multi-task Support:** Time series forecasting, classification, regression, and anomaly detection. **Multi-mode Support:** A large collection of TS models, including statistical models (Prophet, ARIMA, VAR, iForest, etc.), deep learning models(DeepAR, GRU, LSTNet, NBeats, VAE, etc.), and neural architecture search. **Multi-variate Support:** From univariate to multivariate time series. **Covariates Support:** Deep learning models support covariates as input featues for time series forecasting. **Probabilistic Intervals Support:** Time series forecasting visualization can show confidence intervals. **Diversified Preprocessing:** Outlier clipping, missing value imputing, sequence smoothing, normalization, etc. **Abundant Metrics:** A variety of performance metrics to evaluate results and guide models optimization, including MSE, SMAPE, Accuracy, F1-Score,etc. **Powerful Search Strategies:** Adapting Grid Search, Monte Carlo Tree Search, Evolution Algorithm combined with a meta-learner to enable a powerful and effective TS pipeline. **Ensemble:** Refinement greedy emsemble glues the most powerful models. **Cross Validation:** Multiple time series cross-validation strategies ensure generalization ability. ## Communication - If you wish to contribute to this project, please refer to [CONTRIBUTING](CONTRIBUTING.md). - If you have any question or idea, you can alse participate with our [Discussions Community](https://github.com/DataCanvasIO/HyperTS/discussions). ## HyperTS Related Projects * [Hypernets](https://github.com/DataCanvasIO/Hypernets): A general automated machine learning (AutoML) framework. * [HyperGBM](https://github.com/DataCanvasIO/HyperGBM): A full pipeline AutoML tool integrated various GBM models. * [HyperDT/DeepTables](https://github.com/DataCanvasIO/DeepTables): An AutoDL tool for tabular data. * [HyperKeras](https://github.com/DataCanvasIO/HyperKeras): An AutoDL tool for Neural Architecture Search and Hyperparameter Optimization on Tensorflow and Keras. * [HyperBoard](https://github.com/DataCanvasIO/HyperBoard): A visualization tool for Hypernets. * [Cooka](https://github.com/DataCanvasIO/Cooka): Lightweight interactive AutoML system.  ## Citation If you use HyperTS in your research, please cite us as follows: Xiaojing Zhang，Haifeng Wu，Jian Yang. **HyperTS: A Full-Pipeline Automated Time Series Analysis Toolkit.** https://github.com/DataCanvasIO/HyperTS. 2022. Version 0.2.x. BibTex: ``` @misc{hyperts, author={Xiaojing Zhang，Haifeng Wu，Jian Yang.}, title={{HyperTS}: { A Full-Pipeline Automated Time Series Analysis Toolkit}}, howpublished={https://github.com/DataCanvasIO/HyperTS}, note={Version 0.2.x}, year={2022} } ``` ## DataCanvas  HyperTS is an open source project created by [DataCanvas](https://www.datacanvas.com/).