Documentation Index
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TimeGPT
StatsForecast
MLForecast
NeuralForecast
HierarchicalForecast
UtilsForecast
DatasetsForecast
SynForecast
CoreForecast

Statistical ⚡️ Forecast

Getting Started

Install | StatsForecast
Quick Start | StatsForecast
End to End Walkthrough | StatsForecast
End to End Walkthrough with Polars

Tutorials

Anomaly Detection
Conformal Prediction
Cross validation | StatsForecast
Electricity Load Forecast | StatsForecast
Detect Demand Peaks | StatsForecast
Volatility forecasting (GARCH & ARCH)
Intermittent or Sparse Data
MLFlow | StatsForecast
Multiple seasonalities
Statistical, Machine Learning and Neural Forecasting methods | StatsForecast
Probabilistic Forecasting | StatsForecast
Trajectory Simulation

How to Guides

Automatic Time Series Forecasting
Exogenous Regressors
Generating features
Sklearn models

Distributed

Dask
Ray
Spark

Experiments

Amazon Forecast vs StatsForecast
AutoARIMA Comparison (Prophet and pmdarima)
AutoARIMAProphet Adapter
Forecasting at Scale using ETS and ray (M5)
StatsForecast ETS and Facebook Prophet on Spark (M5)

Model References

ADIDA Model
ARCH Model
ARIMA Model
AutoARIMA Model
AutoCES Model
AutoETS Model
AutoRegressive Model
AutoTheta Model
Conformal Seasonal Pool (CSP)
CrostonClassic Model
CrostonOptimized Model
CrostonSBA Model
Dynamic Optimized Theta Model
Dynamic Standard Theta Model
GARCH Model
Holt Model
Holt Winters Model
IMAPA Model
MFLES
Multiple Seasonal Trend (MSTL)
Optimized Theta Model
Seasonal Exponential Smoothing Model
Seasonal Exponential Smoothing Optimized Model
Simple Exponential Smoothing Optimized Model
Simple Exponential Smoothing Model
Standard Theta Model
TSB Model

API Reference

Core Methods
Fugue Backend
Models
StatsForecast's Models
Feature engineering | StatsForecast

Contributing

How to Contribute
Writing Documentation
Interpreting Issue Labels
Submit an Issue
Step-by-step Contribution Guide
Contributing Code to Nixtla Development

Contributing

Nixtla Documentation

TBD

Related topics

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