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An exponential moving average (EMA) is a type of moving average that gives more weight to recent observations. In R, the EMA can be calculated using the ‘SMA’ function from the ‘TTR’ package. The function requires the data, the window size, and the type of averaging (simple or exponential) as inputs. The output of the function is the EMA for each value in the data. The EMA is weighted such that more recent observations are given more weight than older observations.
In time series analysis, a moving average is simply the average value of a certain number of previous periods.
An exponential moving average is a type of moving average that gives more weight to recent observations, which means it’s able to capture recent trends more quickly.
This tutorial explains how to calculate an exponential moving average in R.
Example: Exponential Moving Average in R
Suppose we have the following data frame in R:
#create data frame df <- data.frame(period=1:10, sales=c(25, 20, 14, 16, 27, 20, 12, 15, 14, 19)) #view data frame df period sales 1 1 25 2 2 20 3 3 14 4 4 16 5 5 27 6 6 20 7 7 12 8 8 15 9 9 14 10 10 19
We can use the movavg() function from the pracma package to calculate the exponentially weighted moving average for a certain number of previous periods.
This function uses the following syntax:
movavg(x, n, type=c(“s”, “t”, “w”, “m”, “e”, “r”))
where:
- x: Time series as numeric vector
- n: Number of previous periods to use for average
- type: Type of moving average to calculate. We will use “e” for exponential weighted moving average.
For example, here’s how to calculate the exponentially weighted moving average using the four previous periods:
library(pracma) #create new column to hold 4-day exponentially weighted moving average df$EWM_4day <- movavg(df$sales, n=4, type='e') #view DataFrame df period sales 4dayEWM 0 1 25 25.000000 1 2 20 23.000000 2 3 14 19.400000 3 4 16 18.040000 4 5 27 21.624000 5 6 20 20.974400 6 7 12 17.384640 7 8 15 16.430784 8 9 14 15.458470 9 10 19 16.875082
We can also use the ggplot2 visualization library to visualize the sales compared to the 4-day exponentially weighted moving average:
library(ggplot2)
library(reshape2)
#melt data into format for easy plotting
df <- melt(df , id.vars = 'period', variable.name = 'series')
#plot sales vs. 4-day exponentially weighted moving average
ggplot(df, aes(period, value)) +
geom_line(aes(colour = series))
The red line displays the sales during each period and the blue line displays the exponentially weighted moving average.
How to Plot Multiple Columns in R
How to Average Across Columns in R
How to Calculate the Mean by Group in R