Home

smooth Wiki

Welcome to the smooth package wiki! The smooth package implements Single Source of Error (SSOE) state-space models for forecasting and time series analysis, available for both R and Python (under development).

Quick Links

• CRAN Package
• PyPI Package
• GitHub Repository
• ADAM Online Book

Main Forecasting Functions

Function	Description	Python	R
ADAM	Augmented Dynamic Adaptive Model - unified ETS/ARIMA/regression framework	Yes	Yes
auto.adam	Automatic ADAM with distribution and ARIMA order selection	TBA	Yes
ES	Exponential Smoothing (ETS) wrapper for ADAM	Yes	Yes
CES	Complex Exponential Smoothing	TBA	Yes
SSARIMA	State Space ARIMA	TBA	Yes
MSARIMA	Multiple Seasonal ARIMA	TBA	Yes
GUM	Generalised Univariate Model	TBA	Yes
SMA	Simple Moving Average	TBA	Yes
OES	Occurrence ETS for intermittent demand	TBA	Yes

Utility Functions

Function	Description	Python	R
msdecompose	Multiple seasonal decomposition (used for ADAM/ES initialization)	Yes	Yes
lowess	Scatter plot smoothing from Cleveland, W. S. (1979).	Yes	in stats package

lowess was implemented in Python to fully reproduce R's behaviour. The other existing Python implementations were not as accurate.

Getting Started

Installation

See Installation for details.

Python

from smooth import ADAM, ES, msdecompose

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

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

# Time series decomposition
result = msdecompose(y, lags=[12], type='additive')

R

library(smooth)

# Automatic model selection
model <- adam(y, model="ZXZ", lags=12)
forecast(model, h=12)

# Automatic distribution and ARIMA selection
model <- auto.adam(y, model="ZZZ",
                   orders=list(ar=2, i=2, ma=2, select=TRUE),
                   distribution=c("dnorm","dlaplace","ds"))

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

Recommended Function

ADAM is the recommended function for most forecasting tasks. It provides:

• Unified ETS and ARIMA framework
• Multiple seasonality support
• Various error distributions
• Intermittent demand handling
• External regressors
• Automatic model selection
• ... and more

Common Parameters

These pages document parameters shared across multiple functions:

• Model-Specification - The model parameter, ETS taxonomy, and model type methods
• Orders-and-Lags - ARIMA orders and seasonal lags
• Loss-Functions - Loss functions for estimation (MSE, MAE, likelihood, etc.)
• Explanatory-Variables - External regressors via formula and xreg
• Initialisation - The initial parameter and state initialization
• Persistence - Smoothing parameters (alpha, beta, gamma) and phi
• Bounds - Parameter bounds and stability restrictions

Methods and Tools

Output and Visualisation

• Visualisation-and-Output - plot(), print(), summary(), xtable()

Parameters and Forecasts

• Coefficients-and-Parameters - coef(), confint(), vcov(), coefbootstrap()
• Fitted-Values-and-Forecasts - fitted(), actuals(), predict(), forecast()

Residuals and Diagnostics

• Residuals-and-Errors - residuals(), rstandard(), rmultistep(), outlierdummy()

Model Comparison

• Likelihood-and-Information-Criteria - logLik(), AIC(), BIC(), accuracy(), pls()

Model Information

• Model-Information - nobs(), nparam(), sigma(), extractScale()

Advanced Methods

• Simulation-Functions - simulate(), sim.es(), sim.ssarima(), sim.ces(), sim.gum(), sim.sma()
• Refitting-and-Reforecasting - reapply(), reforecast()
• Scale-Model - sm() for modelling heteroscedasticity

R vs Python Comparison

Main Functions

Function	R	Python	Wiki
adam()	Yes	ADAM class	ADAM
auto.adam()	Yes	TBA	auto.adam
es()	Yes	ES class	ES
ces()	Yes	TBA	CES
gum()	Yes	TBA	GUM
ssarima()	Yes	TBA	SSARIMA
msarima()	Yes	TBA	MSARIMA
sma()	Yes	TBA	SMA
oes()	Yes	TBA	OES
msdecompose()	Yes	Yes	msdecompose
lowess()	in stats	Yes	-

Methods

Fitting and Forecasting:

Method	R	Python	Description
fit	adam()	.fit()	Fit the model
predict	forecast() / predict()	.predict()	Generate forecasts
fitted	Yes	.fitted	Fitted values
actuals	Yes	.actuals	Original data
simulate	Yes	TBA	Simulate from model

Output and Visualisation:

Method	R	Python	Description
summary	Yes	TBA	Model summary with confidence intervals etc
print	Yes	print()	Basic output
plot	Yes	TBA	Diagnostic plots
xtable	Yes	TBA	LaTeX tables

Coefficients and Parameters:

Method	R	Python	Description
coef	Yes	.coef	Extract coefficients
confint	Yes	TBA	Confidence intervals
vcov	Yes	TBA	Variance-covariance matrix
coefbootstrap	Yes	TBA	Bootstrap coefficients

Residuals and Errors:

Method	R	Python	Description
residuals	Yes	.residuals	Model residuals
rstandard	Yes	TBA	Standardised residuals
rstudent	Yes	TBA	Studentised residuals
rmultistep	Yes	TBA	Multi-step forecast errors
multicov	Yes	TBA	Multi-step error covariance
outlierdummy	Yes	TBA	Outlier dummy variables

Likelihood and Information Criteria:

Method	R	Python	Description
logLik	Yes	TBA	Log-likelihood
AIC / BIC	Yes	TBA	Information criteria
AICc / BICc	Yes	TBA	Corrected IC
accuracy	Yes	TBA	Forecast accuracy measures
pls	Yes	TBA	Prediction likelihood score

Model Information:

Method	R	Python	Description
nobs	Yes	.nobs	Number of observations
nparam	Yes	.nparam	Number of parameters
sigma	Yes	.sigma	Residual standard deviation
errorType	Yes	.error_type	Error type (A/M)
modelType	Yes	.model_type	Model specification
modelName	Yes	.model_name	Full model name
orders	Yes	.orders	ARIMA orders
lags	Yes	.lags	Model lags

Advanced Methods:

Method	R	Python	Description
reapply	Yes	TBA	Refit with perturbed parameters
reforecast	Yes	TBA	Forecast with parameter uncertainty
sm	Yes	TBA	Scale model (heteroscedasticity)

Additional Resources

• Installation - Installation instructions and troubleshooting
• Model-Estimation - Optimisation and estimation parameters
• Resources - Publications and DOIs for each function