💫 Easy-to-use, powerful, and unified full pipeline automated time series toolkit. Supports forecasting, classification, regression, and anomaly detection.
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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.
Note:
- Prophet is required by HyperTS, install it from
condabefore installing HyperTS usingpip. - 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:
pip install hypertsYou can also install HyperTS from conda via the conda-forge channel:
conda install -c conda-forge hypertsIf you would like the most up-to-date version, you can instead install direclty from github:
git clone git@github.com:DataCanvasIO/HyperTS.git
cd HyperTS
pip install -e .
pip install tensorflow #optional, recommended version: >=2.0.0,<=2.10.0For more installation tips, see installation.
| English Docs / Chinese Docs | Discription |
|---|---|
| Expected Data Format | What data formats do HyperTS expect? |
| Quick Start | How to get started quickly with HyperTS? |
| Advanced Ladder | How to realize the potential of HyperTS? |
| Custom Functions | How to customize the functions of HyperTS? |
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.
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)
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)
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)
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.
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.
- If you wish to contribute to this project, please refer to CONTRIBUTING.
- If you have any question or idea, you can alse participate with our Discussions Community.
- Hypernets: A general automated machine learning (AutoML) framework.
- HyperGBM: A full pipeline AutoML tool integrated various GBM models.
- HyperDT/DeepTables: An AutoDL tool for tabular data.
- HyperKeras: An AutoDL tool for Neural Architecture Search and Hyperparameter Optimization on Tensorflow and Keras.
- HyperBoard: A visualization tool for Hypernets.
- Cooka: Lightweight interactive AutoML system.
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}
}
HyperTS is an open source project created by DataCanvas.