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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.
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.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
