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 | ✅ | ✅ |