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<div align="center"> <img src="https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/imgs_indx/logo_mid.png"/> <h1 align="center">Statistical ⚡️ Forecast</h1> <h3 align="center">Lightning fast forecasting with statistical and econometric models</h3>

StatsForecast offers a collection of widely used univariate time series forecasting models, including automatic ARIMA, ETS, CES, and Theta modeling optimized for high performance. It also includes a large battery of benchmarking models.

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Installation

You can install StatsForecast with:

pip install statsforecast

or

conda install -c conda-forge statsforecast

Vist our Installation Guide for further instructions.

Quick Start

Minimal Example

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 quick guide

Follow this end-to-end walkthrough 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 includes an extensive battery of models that can efficiently fit millions of time series.

Features

• Fastest and most accurate implementations of AutoARIMA, AutoETS, AutoCES, MSTL and 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 than pmdarima.
• 1.5x faster than R.
• 500x faster than Prophet.
• 4x faster than statsmodels.
• 1,000,000 series in 30 min with ray.
• Replace FB-Prophet in two lines of code and gain speed and accuracy. Check the experiments here.
• Fit 10 benchmark models on 1,000,000 series in under 5 min.

Missing something? Please open an issue or write us in

Examples and Guides

📚 End to End Walkthrough: Model training, evaluation and selection for multiple time series

🔎 Anomaly Detection: detect anomalies for time series using in-sample prediction intervals.

👩‍🔬 Cross Validation: robust model’s performance evaluation.

❄️ Multiple Seasonalities: how to forecast data with multiple seasonalities using an MSTL.

🔌 Predict Demand Peaks: electricity load forecasting for detecting daily peaks and reducing electric bills.

📈 Intermittent Demand: forecast series with very few non-zero observations.

🌡️ Exogenous Regressors: 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|✅|✅|✅|✅|✅| |AutoETS|✅|✅|✅|✅|| |AutoCES|✅|✅|✅|✅|| |AutoTheta|✅|✅|✅|✅|| |AutoMFLES|✅|✅|✅|✅|✅| |AutoTBATS|✅|✅|✅|✅||

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|✅|✅|✅|✅|✅| |AutoRegressive|✅|✅|✅|✅|✅|

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|✅|✅|✅|✅|✅| |OptimizedTheta|✅|✅|✅|✅|| |DynamicTheta|✅|✅|✅|✅|| |DynamicOptimizedTheta|✅|✅|✅|✅||

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|✅|✅|✅|✅|If trend forecaster supports| |MFLES|✅|✅|✅|✅|✅| |TBATS|✅|✅|✅|✅||

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|✅|✅|✅|✅|| |ARCH|✅|✅|✅|✅||

Baseline Models

Classical models for establishing baseline.

|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features| |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:| |HistoricAverage|✅|✅|✅|✅|| |[Naive](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.htm