> ## Documentation Index > Fetch the complete documentation index at: https://nixtla-old-docs.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. > StatsForecast offers a collection of popular univariate time series forecasting models optimized for high performance and scalability. # StatsForecast ⚑️ ## Installation You can install [`StatsForecast`](https://Nixtla.github.io/statsforecast/src/core/core.html#statsforecast) with: ```python theme={null} pip install statsforecast ``` or ```python theme={null} conda install -c conda-forge statsforecast ``` Vist our [Installation Guide](./docs/getting-started/installation.html) for further instructions. ## Quick Start **Minimal Example** ```python theme={null} from statsforecast import StatsForecast from statsforecast.models import AutoARIMA from statsforecast.utils import AirPassengersDF df = AirPassengersDF sf = StatsForecast( models=[AutoARIMA(season_length=12)], freq='ME', ) sf.fit(df) sf.predict(h=12, level=[95]) ``` **Get Started with this [quick guide](docs/getting-started/getting_started_short.html).** **Follow this [end-to-end walkthrough](docs/getting-started/getting_started_complete.html) for best practices.** ## Why? Current Python alternatives for statistical models are slow, inaccurate and don’t scale well. So we created a library that can be used to forecast in production environments or as benchmarks. [`StatsForecast`](https://Nixtla.github.io/statsforecast/src/core/core.html#statsforecast) includes an extensive battery of models that can efficiently fit millions of time series. ## Features * Fastest and most accurate implementations of [`AutoARIMA`](https://Nixtla.github.io/statsforecast/src/core/models.html#autoarima), [`AutoETS`](https://Nixtla.github.io/statsforecast/src/core/models.html#autoets), [`AutoCES`](https://Nixtla.github.io/statsforecast/src/core/models.html#autoces), [`MSTL`](https://Nixtla.github.io/statsforecast/src/core/models.html#mstl) and [`Theta`](https://Nixtla.github.io/statsforecast/src/core/models.html#theta) in Python. * Out-of-the-box compatibility with Spark, Dask, and Ray. * Probabilistic Forecasting and Confidence Intervals. * Support for exogenous Variables and static covariates. * Anomaly Detection. * Familiar sklearn syntax: `.fit` and `.predict`. ## Highlights * Inclusion of `exogenous variables` and `prediction intervals` for ARIMA. * 20x [faster](https://github.com/Nixtla/statsforecast/tree/main/experiments/arima) than `pmdarima`. * 1.5x faster than `R`. * 500x faster than `Prophet`. * 4x [faster](https://github.com/Nixtla/statsforecast/tree/main/experiments/ets) than `statsmodels`. * Compiled to high performance machine code through [`numba`](https://numba.pydata.org/). * 1,000,000 series in [30 min](https://github.com/Nixtla/statsforecast/tree/main/experiments/ray) with [ray](https://github.com/ray-project/ray). * Replace FB-Prophet in two lines of code and gain speed and accuracy. Check the experiments [here](https://github.com/Nixtla/statsforecast/tree/main/experiments/arima_prophet_adapter). * Fit 10 benchmark models on **1,000,000** series in [under **5 min**](https://github.com/Nixtla/statsforecast/tree/main/experiments/benchmarks_at_scale). Missing something? Please open an issue or write us in [![Slack](https://img.shields.io/badge/Slack-4A154B?\&logo=slack\&logoColor=white.png)](https://join.slack.com/t/nixtlaworkspace/shared_invite/zt-135dssye9-fWTzMpv2WBthq8NK0Yvu6A) ## Examples and Guides πŸ“š [End to End Walkthrough](https://nixtla.github.io/statsforecast/docs/getting-started/getting_started_complete.html): Model training, evaluation and selection for multiple time series πŸ”Ž [Anomaly Detection](https://nixtla.github.io/statsforecast/docs/tutorials/anomalydetection.html): detect anomalies for time series using in-sample prediction intervals. πŸ‘©β€πŸ”¬ [Cross Validation](https://nixtla.github.io/statsforecast/docs/tutorials/crossvalidation.html): robust model’s performance evaluation. ❄️ [Multiple Seasonalities](https://nixtla.github.io/statsforecast/docs/tutorials/multipleseasonalities.html): how to forecast data with multiple seasonalities using an MSTL. πŸ”Œ [Predict Demand Peaks](https://nixtla.github.io/statsforecast/docs/tutorials/electricitypeakforecasting.html): electricity load forecasting for detecting daily peaks and reducing electric bills. πŸ“ˆ [Intermittent Demand](https://nixtla.github.io/statsforecast/docs/tutorials/intermittentdata.html): forecast series with very few non-zero observations. 🌑️ [Exogenous Regressors](https://nixtla.github.io/statsforecast/docs/how-to-guides/exogenous.html): like weather or prices ## Models ### Automatic Forecasting Automatic forecasting tools search for the best parameters and select the best possible model for a group of time series. These tools are useful for large collections of univariate time series. | Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features | | :-------------------------------------------------------------------------------------- | :------------: | :--------------------: | :--------------------: | :-------------------------: | :----------------: | | [AutoARIMA](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoarima) | βœ… | βœ… | βœ… | βœ… | βœ… | | [AutoETS](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoets) | βœ… | βœ… | βœ… | βœ… | | | [AutoCES](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoces) | βœ… | βœ… | βœ… | βœ… | | | [AutoTheta](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autotheta) | βœ… | βœ… | βœ… | βœ… | | | [AutoMFLES](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#automfles) | βœ… | βœ… | βœ… | βœ… | βœ… | | [AutoTBATS](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autotbats) | βœ… | βœ… | βœ… | βœ… | | ### ARIMA Family These models exploit the existing autocorrelations in the time series. | Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features | | :------------------------------------------------------------------------------------------------ | :------------: | :--------------------: | :--------------------: | :-------------------------: | :----------------: | | [ARIMA](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#arima) | βœ… | βœ… | βœ… | βœ… | βœ… | | [AutoRegressive](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoregressive) | βœ… | βœ… | βœ… | βœ… | βœ… | ### Theta Family Fit two theta lines to a deseasonalized time series, using different techniques to obtain and combine the two theta lines to produce the final forecasts. | Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features | | :-------------------------------------------------------------------------------------------------------------- | :------------: | :--------------------: | :--------------------: | :-------------------------: | :----------------: | | [Theta](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#theta) | βœ… | βœ… | βœ… | βœ… | | | [OptimizedTheta](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#optimizedtheta) | βœ… | βœ… | βœ… | βœ… | | | [DynamicTheta](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#dynamictheta) | βœ… | βœ… | βœ… | βœ… | | | [DynamicOptimizedTheta](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#dynamicoptimizedtheta) | βœ… | βœ… | βœ… | βœ… | | ### Multiple Seasonalities Suited for signals with more than one clear seasonality. Useful for low-frequency data like electricity and logs. | Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features | | :------------------------------------------------------------------------------ | :------------: | :--------------------: | :--------------------: | :-------------------------: | :--------------------------: | | [MSTL](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#mstl) | βœ… | βœ… | βœ… | βœ… | If trend forecaster supports | | [MFLES](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#mfles) | βœ… | βœ… | βœ… | βœ… | βœ… | | [TBATS](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#tbats) | βœ… | βœ… | βœ… | βœ… | | ### GARCH and ARCH Models Suited for modeling time series that exhibit non-constant volatility over time. The ARCH model is a particular case of GARCH. | Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features | | :------------------------------------------------------------------------------ | :------------: | :--------------------: | :--------------------: | :-------------------------: | :----------------: | | [GARCH](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#garch) | βœ… | βœ… | βœ… | βœ… | | | [ARCH](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#arch) | βœ… | βœ… | βœ… | βœ… | | ### Baseline Models Classical models for establishing baseline. | Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features | | :-------------------------------------------------------------------------------------------------------------- | :------------: | :--------------------: | :--------------------: | :-------------------------: | :----------------: | | [HistoricAverage](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#historicaverage) | βœ… | βœ… | βœ… | βœ… | | | [Naive](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#naive) | βœ… | βœ… | βœ… | βœ… | | | [RandomWalkWithDrift](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#randomwalkwithdrift) | βœ… | βœ… | βœ… | βœ… | | | [SeasonalNaive](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#seasonalnaive) | βœ… | βœ… | βœ… | βœ… | | | [WindowAverage](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#windowaverage) | βœ… | | | | | | [SeasonalWindowAverage](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#seasonalwindowaverage) | βœ… | | | | | ### Exponential Smoothing Uses a weighted average of all past observations where the weights decrease exponentially into the past. Suitable for data with clear trend and/or seasonality. Use the `SimpleExponential` family for data with no clear trend or seasonality. | Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features | | :---------------------------------------------------------------------------------------------------------------------------------------------- | :------------: | :--------------------: | :--------------------: | :-------------------------: | :----------------: | | [SimpleExponentialSmoothing](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#simpleexponentialsmoothing) | βœ… | | | | | | [SimpleExponentialSmoothingOptimized](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#simpleexponentialsmoothingoptimized) | βœ… | | | | | | [SeasonalExponentialSmoothing](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#seasonalexponentialsmoothing) | βœ… | | | | | | [SeasonalExponentialSmoothingOptimized](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#seasonalexponentialsmoothingoptimized) | βœ… | | | | | | [Holt](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#holt) | βœ… | βœ… | βœ… | βœ… | | | [HoltWinters](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#holtwinters) | βœ… | βœ… | βœ… | βœ… | | ### Sparse or Inttermitent Suited for series with very few non-zero observations | Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features | | :---------------------------------------------------------------------------------------------------- | :------------: | :--------------------: | :--------------------: | :-------------------------: | :----------------: | | [ADIDA](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#adida) | βœ… | | βœ… | βœ… | | | [CrostonClassic](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#crostonclassic) | βœ… | | βœ… | βœ… | | | [CrostonOptimized](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#crostonoptimized) | βœ… | | βœ… | βœ… | | | [CrostonSBA](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#crostonsba) | βœ… | | βœ… | βœ… | | | [IMAPA](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#imapa) | βœ… | | βœ… | βœ… | | | [TSB](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#tsb) | βœ… | | βœ… | βœ… | | ### Machine Learning Leverage exogenous features. | Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features | | :-------------------------------------------------------------------------------------------- | :------------: | :--------------------: | :--------------------: | :-------------------------: | :----------------: | | [SklearnModel](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#sklearnmodel) | βœ… | | βœ… | βœ… | βœ… | ## How to contribute See [CONTRIBUTING.md](https://github.com/Nixtla/statsforecast/blob/main/CONTRIBUTING.md). ## Citing ```bibtex theme={null} @misc{garza2022statsforecast, author={Federico Garza, Max Mergenthaler Canseco, Cristian ChallΓΊ, Kin G. Olivares}, title = {{StatsForecast}: Lightning fast forecasting with statistical and econometric models}, year={2022}, howpublished={{PyCon} Salt Lake City, Utah, US 2022}, url={https://github.com/Nixtla/statsforecast} } ```