**Darts** is a Python library for user-friendly forecasting and anomaly detection on time series. It contains a variety of models, from classics such as ARIMA to deep neural networks.

Some of the key features of Darts include:

- A simple and intuitive interface for defining and fitting models
- Support for different types of time series data, including univariate, multivariate, and panel data
- A wide range of built-in models, including ARIMA, Exponential Smoothing, Prophet, LSTM, and TCN
- Tools for hyperparameter tuning and model selection, such as cross-validation and grid search
- Visualization tools for exploring and analyzing time series data and model outputs

Model | Univariate | Multivariate | Probabilistic | Multiple series (global) | Past-observed covariates | Future-known covariates | Static covariates | Reference |
---|---|---|---|---|---|---|---|---|

`ARIMA` | ✅ | ✅ | ✅ | |||||

`VARIMA` | ✅ | ✅ | ✅ | |||||

`AutoARIMA` | ✅ | ✅ | ||||||

`StatsForecastAutoARIMA` (faster AutoARIMA) | ✅ | ✅ | ✅ | Nixtla’s statsforecast | ||||

`ExponentialSmoothing` | ✅ | ✅ | ||||||

`StatsForecastETS` | ✅ | ✅ | Nixtla’s statsforecast | |||||

`BATS` and `TBATS` | ✅ | ✅ | TBATS paper | |||||

`Theta` and `FourTheta` | ✅ | Theta & 4 Theta | ||||||

`Prophet` (see install notes) | ✅ | ✅ | ✅ | Prophet repo | ||||

`FFT` (Fast Fourier Transform) | ✅ | |||||||

`KalmanForecaster` using the Kalman filter and N4SID for system identification | ✅ | ✅ | ✅ | ✅ | N4SID paper | |||

`Croston` method | ✅ | |||||||

`RegressionModel` ; generic wrapper around any sklearn regression model | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ||

`RandomForest` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ||

`LinearRegressionModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |

`LightGBMModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |

`CatBoostModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |

`XGBModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |

`RNNModel` (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version | ✅ | ✅ | ✅ | ✅ | ✅ | DeepAR paper | ||

`BlockRNNModel` (incl. LSTM and GRU) | ✅ | ✅ | ✅ | ✅ | ✅ | |||

`NBEATSModel` | ✅ | ✅ | ✅ | ✅ | ✅ | N-BEATS paper | ||

`NHiTSModel` | ✅ | ✅ | ✅ | ✅ | ✅ | N-HiTS paper | ||

`TCNModel` | ✅ | ✅ | ✅ | ✅ | ✅ | TCN paper, DeepTCN paper, blog post | ||

`TransformerModel` | ✅ | ✅ | ✅ | ✅ | ✅ | |||

`TFTModel` (Temporal Fusion Transformer) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | TFT paper, PyTorch Forecasting |

`DLinearModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | DLinear paper |

`NLinearModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | NLinear paper |

Naive Baselines | ✅ | ✅ |