Documentation Index
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TimeGPT
StatsForecast
MLForecast
NeuralForecast
HierarchicalForecast
UtilsForecast
DatasetsForecast
CoreForecast

StatsForecast ⚡️

Getting Started

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

Tutorials

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

How to Guides

Automatic Time Series Forecasting
Exogenous Regressors
Generating features
Sklearn models
Migrating from R
Numba caching

Distributed

Dask
Ray
Spark

Experiments

Amazon Forecast vs StatsForecast
AutoARIMA Comparison (Prophet and pmdarima)
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
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
FugueBackend
Models
StatsForecast's Models
Feature engineering

Contributing

How to Contribute
Writing Documentation
Interpreting Issue Labels
Submit an Issue
Step by step
Techstack

MFLES

MFLES model

MFLES

 MFLES (verbose=1, robust=None)

Initialize self. See help(type(self)) for accurate signature.

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