Forecasting Using State Space Models

Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting. The package includes ADAM (Svetunkov, 2023, < https://openforecast.org/adam/>), Exponential Smoothing (Hyndman et al., 2008, ), SARIMA (Svetunkov & Boylan, 2019 ), Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, ), Simple Moving Average (Svetunkov & Petropoulos, 2018 ) and several simulation functions. It also allows dealing with intermittent demand based on the iETS framework (Svetunkov & Boylan, 2019, ).

smooth

R:

Python:

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The smooth package implements Single Source of Error (SSOE) state-space models for forecasting and time series analysis, available for both R and Python.

Both the R and Python versions of smooth depend on the greybox package for distributions, information criteria, and supporting utilities (in Python this also provides the LOWESS smoother). It is installed automatically with smooth.

Installation

R (CRAN):

install.packages("smooth")

R (github):

if (!require("remotes")) install.packages("remotes")
remotes::install_github("openforecast-org/smooth")

Python (PyPI):

pip install smooth

Python (github, dev):

pip install "git+https://github.com/openforecast-org/smooth.git@master#subdirectory=python"

For development versions and system requirements, see the Installation wiki page.

Quick Examples

R

library(smooth)

# ADAM - the recommended function for most tasks
model <- adam(y, model="ZXZ", lags=12)
forecast(model, h=12)

# Exponential Smoothing
model <- es(y, model="ZXZ", lags=12)

# Automatic model selection for ETS+ARIMA and distributions
model <- auto.adam(y, model="ZZZ",
                   orders=list(ar=2, i=2, ma=2, select=TRUE))

Python

from smooth import ADAM, ES

# ADAM model
model = ADAM(model="ZXZ", lags=12)
model.fit(y)
model.predict(h=12)

# Exponential Smoothing
model = ES(model="ZXZ")
model.fit(y)

Documentation

Full documentation is available on the GitHub Wiki, including:

• ADAM - Main unified ETS/ARIMA framework
• Function reference - All functions and methods
• Installation guide - Dependencies and troubleshooting

Book: Svetunkov, I. (2023). Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM). Chapman and Hall/CRC. Online: https://openforecast.org/adam/

About

smooth is developed and maintained by OpenForecast, a demand forecasting and inventory management consultancy. The package implements the methods we use in our consulting and teach in our training courses.