darts
A python library for user-friendly forecasting and anomaly detection on time series.
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- __init__.py
- aggregators.py
- and_aggregator.py
- ensemble_sklearn_aggregator.py
- or_aggregator.py
- __init__.py
- anomaly_model.py
- filtering_am.py
- forecasting_am.py
- __init__.py
- detectors.py
- iqr_detector.py
- quantile_detector.py
- threshold_detector.py
- __init__.py
- difference_scorer.py
- kmeans_scorer.py
- nll_cauchy_scorer.py
- nll_exponential_scorer.py
- nll_gamma_scorer.py
- nll_gaussian_scorer.py
- nll_laplace_scorer.py
- nll_poisson_scorer.py
- norm_scorer.py
- pyod_scorer.py
- scorers.py
- wasserstein_scorer.py
- __init__.py
- utils.py
- __init__.py
- _plot.py
- cost_matrix.py
- dtw.py
- window.py
- __init__.py
- encoder_base.py
- encoders.py
- __init__.py
- base_data_transformer.py
- boxcox.py
- diff.py
- fittable_data_transformer.py
- invertible_data_transformer.py
- mappers.py
- midas.py
- missing_values_filler.py
- reconciliation.py
- scaler.py
- static_covariates_transformer.py
- window_transformer.py
- __init__.py
- pipeline.py
- __init__.py
- dataset_loaders.py
- datasets.py
- __init__.py
- shap_adapter.py
- sklearn_shap_adapter.py
- torch_shap_adapter.py
- __init__.py
- explainability.py
- explainability_result.py
- shap_explainer.py
- tft_explainer.py
- utils.py
- __init__.py
- metrics.py
- utils.py
- __init__.py
- chronos2_submodels.py
- feed_forward.py
- glu_variants.py
- huggingface_connector.py
- layer_norm_variants.py
- patchtst_fm_submodels.py
- tft_submodels.py
- timesfm2p5_submodels.py
- transformer.py
- __init__.py
- filtering_model.py
- gaussian_process_filter.py
- kalman_filter.py
- moving_average_filter.py
- __init__.py
- arima.py
- baselines.py
- block_rnn_model.py
- catboost_model.py
- chronos2_model.py
- conformal_models.py
- dlinear.py
- ensemble_model.py
- exponential_smoothing.py
- fft.py
- forecasting_model.py
- foundation_model.py
- global_baseline_models.py
- kalman_forecaster.py
- lgbm.py
- linear_regression_model.py
- multivariate_model.py
- naive_ensemble_model.py
- nbeats.py
- nf_model.py
- nhits.py
- nlinear.py
- patchtst_fm_model.py
- pl_forecasting_module.py
- prophet_model.py
- random_forest.py
- regression_ensemble_model.py
- rnn_model.py
- sf_auto_arima.py
- sf_auto_ces.py
- sf_auto_ets.py
- sf_auto_mfles.py
- sf_auto_tbats.py
- sf_auto_theta.py
- sf_croston.py
- sf_model.py
- sf_tbats.py
- sklearn_model.py
- tcn_model.py
- tft_model.py
- theta.py
- tide_model.py
- timesfm2p5_model.py
- tirex_model.py
- torch_forecasting_model.py
- transformer_model.py
- tsmixer_model.py
- varima.py
- xgboost.py
- __init__.py
- __init__.py
- test_aggregators.py
- test_anomaly_model.py
- test_detectors.py
- test_evaluation.py
- test_scorers.py
- __init__.py
- test_dtw.py
- __init__.py
- test_covariate_index_generators.py
- test_encoders.py
- __init__.py
- test_base_data_transformer.py
- test_boxcox.py
- test_data_transformer.py
- test_diff.py
- test_fittable_data_transformer.py
- test_invertible_data_transformer.py
- test_invertible_fittable_data_transformer.py
- test_mappers.py
- test_midas.py
- test_missing_values_filler.py
- test_reconciliation.py
- test_static_covariates_transformer.py
- test_window_transformations.py
- __init__.py
- test_pipeline.py
- __init__.py
- test_dataset_loaders.py
- __init__.py
- test_shap_explainability_result.py
- test_shap_explainer.py
- test_tft_explainer.py
- test_torch_explainer.py
- __init__.py
- test_classification_metrics.py
- test_metrics.py
- __init__.py
- glu_variants.py
- test_layer_norm_variants.py
- __init__.py
- test_filters.py
- chronos2.npz
- config.json
- model.safetensors
- patchtstfm.npz
- config.json
- model.safetensors
- timesfm2p5.npz
- config.json
- model.safetensors
- tirex.npz
- __init__.py
- foundation_test_utils.py
- test_4theta.py
- test_backtesting.py
- test_baseline_models.py
- test_block_RNN.py
- test_chronos2.py
- test_classifier_model.py
- test_conformal_model.py
- test_dlinear_nlinear.py
- test_ensemble_models.py
- test_exponential_smoothing.py
- test_fft.py
- test_foundation.py
- test_global_forecasting_models.py
- test_local_forecasting_models.py
- test_multivariate_model.py
- test_nbeats_nhits.py
- test_nf_models.py
- test_patchtst_fm.py
- test_probabilistic_models.py
- test_prophet.py
- test_ptl_trainer.py
- test_regression_ensemble_model.py
- test_residuals.py
- test_RNN.py
- test_sf_models.py
- test_sklearn_models.py
- test_TCN.py
- test_TFT.py
- test_tide_model.py
- test_timesfm2p5.py
- test_tirex.py
- test_torch_forecasting_model.py
- test_transformer_model.py
- test_tsmixer.py
- __init__.py
- __init__.py
- test_onnx.py
- test_optuna.py
- test_ray.py
- __init__.py
- test_historical_forecasts.py
- test_utils.py
- __init__.py
- test_add_static_covariates.py
- test_create_lagged_prediction_data.py
- test_create_lagged_training_data.py
- test_get_feature_times.py
- test_get_shared_times.py
- test_get_shared_times_bounds.py
- test_strided_moving_window.py
- __init__.py
- test_likelihoods.py
- test_sklearn_likelihoods.py
- test_torch_likelihoods.py
- __init__.py
- test_torch_datasets.py
- __init__.py
- test_callbacks.py
- test_formatting.py
- test_lazy.py
- test_losses.py
- test_missing_values.py
- test_model_selection.py
- test_statistics.py
- test_timeseries_generation.py
- test_ts_utils.py
- test_utils.py
- test_utils_torch.py
- __init__.py
- conftest.py
- test_config.py
- test_logging.py
- test_timeseries.py
- test_timeseries_multivariate.py
- test_timeseries_plot.py
- test_timeseries_plotly.py
- test_timeseries_repr_formatting.py
- test_timeseries_static_covariates.py
- __init__.py
- tabularization.py
- __init__.py
- _data_module.py
- dataset.py
- inference_dataset.py
- training_dataset.py
- utils.py
- __init__.py
- utils.py
- __init__.py
- optimized_historical_forecasts_regression.py
- optimized_historical_forecasts_torch.py
- utils.py
- __init__.py
- base.py
- sklearn.py
- statsforecast.py
- torch.py
- __init__.py
- _formatting.py
- _lazy.py
- _plotting.py
- callbacks.py
- losses.py
- missing_values.py
- model_selection.py
- multioutput.py
- onnx_utils.py
- statistics.py
- timeseries_generation.py
- torch.py
- ts_utils.py
- utils.py
- __init__.py
- config.py
- logging.py
- timeseries.py
- typing.py
- Electricity_test.csv
- test.zip
- AirPassengers.csv
- ausbeer.csv
- australian_tourism.csv
- energy_dataset.csv
- ETTh1.csv
- ETTh2.csv
- ETTm1.csv
- ETTm2.csv
- exchange_rate.csv
- gasrate_co2.csv
- heart_rate.csv
- ice_cream_heater.csv
- ILINet.csv
- monthly-milk-incomplete.csv
- monthly-milk.csv
- monthly-sunspots.csv
- taxi_new_york_passengers.csv
- taylor.csv
- temps.csv
- traffic.csv
- us_gasoline.csv
- weather.csv
- wineind.csv
- woolyrnq.csv
- zurich_electricity_consumption.csv
- api.png
- contribute.png
- examples.png
- models.png
- quickstart.png
- user-guide.png
- api.png
- contribute.png
- examples.png
- models.png
- quickstart.png
- user-guide.png
- .gitkeep
- custom.css
- darts-logo-dark.png
- darts-logo-light.png
- docs-favicon.ico
- conf.py
- examples.rst
- index.rst
- userguide.rst
- module.rst_t
- package.rst_t
- covariates-highlevel.png
- prediction_multi.png
- prediction_once.png
- seq_covs_1.png
- seq_covs_single.png
- sequential_training.png
- tfm.png
- top_level.png
- example_ets.png
- example_linreg_quantile.png
- example_mc_dropout.png
- example_tcn_laplace.png
- example_tcn_quantile.png
- multiple-ts.png
- multivariate.png
- forecast_multiv.png
- covariates.md
- faq.md
- forecasting_overview.md
- gpu_and_tpu_usage.md
- hyperparameter_optimization.md
- timeseries.md
- torch_forecasting_models.md
- fix_package_titles.py
- Makefile
- ad_4_sub_modules.png
- ad_inside_anomaly_model.png
- ad_windowing.png
- covariates-highlevel.png
- global_io.png
- global_io_covs.png
- multi_model_ocl2.png
- regression_model_train.png
- seq_dataset_multi_ts.png
- seq_dataset_one_ts.png
- single_model_ocl2.png
- single_model_ocl3.png
- __init__.py
- utils.py
- 00-quickstart.ipynb
- 01-multi-time-series-and-covariates.ipynb
- 02-data-processing.ipynb
- 03-FFT-examples.ipynb
- 04-RNN-examples.ipynb
- 05-TCN-examples.ipynb
- 06-Transformer-examples.ipynb
- 07-NBEATS-examples.ipynb
- 08-DeepAR-examples.ipynb
- 09-DeepTCN-examples.ipynb
- 10-Kalman-filter-examples.ipynb
- 11-GP-filter-examples.ipynb
- 12-Dynamic-Time-Warping-example.ipynb
- 13-TFT-examples.ipynb
- 14-transfer-learning.ipynb
- 15-static-covariates.ipynb
- 16-hierarchical-reconciliation.ipynb
- 17-hyperparameter-optimization.ipynb
- 18-TiDE-examples.ipynb
- 19-EnsembleModel-examples.ipynb
- 20-SKLearnModel-examples.ipynb
- 21-TSMixer-examples.ipynb
- 22-anomaly-detection-examples.ipynb
- 23-Conformal-Prediction-examples.ipynb
- 24-SKLearnClassifierModel-examples.ipynb
- 25-FoundationModel-examples.ipynb
- 26-NeuralForecast-examples.ipynb
- 27-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb
- 28-Explainability-examples.ipynb
- __init__.py
- ad_4_sub_modules.png
- ad_inside_anomaly_model.png
- ad_windowing.png
- darts-logo-trim.png
- example.png
- example_ad.png
- .coveragerc
- .dockerignore
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- .gitattributes
- .gitignore
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- CHANGELOG.md
- CITATION.cff
- CONTRIBUTING.md
- Dockerfile
- INSTALL.md
- LICENSE
- make_dists.sh
- pyproject.toml
- README.md
π Installation Guide
1. Get the code
git clone https://github.com/unit8co/darts
Downloads the entire project code from GitHub to your computer.
cd darts
Moves into the project folder you just downloaded.
2. Official Install Script
Easy RecommendedPrerequisites
- Python 3 Python is required to use pip.
pip install darts
Installs the package published on PyPI directly β no need to clone the source.
After installing, open a new terminal and run the program's version command (e.g. --version) to confirm it worked.
Pulled directly from this repo's README.
3. Docker
EasyPrerequisites
- Git Needed to download the project code from GitHub.
- Docker Desktop Needed to build and run containers. Install it and keep it running in the background.
docker build -t darts .
Builds a runnable image based on the Dockerfile.
docker run -p 8080:80 darts
Runs the built image as an actual container.
Run docker compose ps to check the containers are Up. If the README mentions a port, open http://localhost:PORT in your browser.
4. Python
EasyPrerequisites
pip install darts
Installs the package published on PyPI directly β no need to clone the source.
If it runs without errors and prints output in the terminal, it worked.
Pulled directly from this repo's README.
// repository documentation
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