Last Update: February 6, 2020
Exponential smoothing methods consist of forecast based on previous periods data with exponentially decaying influence the older they become. Their notation is ETS (error, trend, seasonality) where each can be none (N), additive (A), additive damped (Ad), multiplicative (M) or multiplicative damped (Md).
This topic is part of Forecasting Models with R course. Feel free to take a look at Course Curriculum.
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An example of exponential smoothing methods is Brown simple exponential smoothing  which consists of forecast with no trend or seasonal patterns.
1. Method notation.
- ETS(A,N,N): error = additive, trend = none, seasonality = none
2. Formula notation.
Where = step forecast, = current period level forecast, = current period data, = level smoothing coefficient.
3. R script code example.
3.1. Load R package .
3.2. Exponential smoothing methods data reading, training and testing ranges delimiting.
- Data: S&P 500® index replicating ETF (ticker symbol: SPY) daily adjusted close prices (2007-2015).
- Training and testing ranges delimiting not fixed and only included for educational purposes.
data <- read.csv('Exponential-Smoothing-Methods-Data.txt',header=T) spy <- ts(data[,2],frequency=21) spyt <- window(spy,end=c(84,19)) spyf <- window(spy,start=c(84,20))
3.3. Brown simple exponential smoothing method calculation and chart.
brown <- ses(spyt,h=504) plot(brown,main='Brown Simple Exponential Smoothing ETS(A,N,N) Method',ylab='Price',xlab='Month') lines(spyf,lty=3)
 Robert G. Brown. “Exponential Smoothing for Predicting Demand”. Arthur D. Little Inc. 1956.
 Hyndman RJ, Khandakar Y. “Automatic time series forecasting: the forecast package for R”. Journal of Statistical Software. 2008.