## Moving average of previous three values in R

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In the zoo package there is a function called rollmean, which enables you to make moving averages. The `rollmean(x,3)` will take the previous, current and next value (ie 4, 6 and 2) in the table below. This is shown in the second column.

```x   rollmean    ma3
4
6   4.0
2   4.3
5   3.0         4.0
2   6.3         4.3
12  6.0         3.0
4   6.0         6.3
2               6.0
```

I would like to get the same job done, but by averaging out the previous 3 values in the fourth row. This is displayed in the third column. Can anybody tell me the name of the function that will help to accomplish this?

I struggled searching for a simple function for moving averages that had some flexibility to do what I needed. I finally wrote a couple functions extending the one based on the filter function which rinni gives above in the comment (but which itself won't work because it will include the current observation in the 3 period average).

1. Moving average function that includes the current observation

```mav <- function(x,n){filter(x,rep(1/n,n), sides=1)}
```
2. Moving average function that does not include the current observation

```mavback <- function(x,n){
a<-mav(x,1)
b<-mav(x,(n+1))
c<-(1/n)*((n+1)*b - a)
return(c)
}
```
3. Backward looking moving average function, not including current obs, based on [h2] readings starting [h1] periods back

```mavback1<-function(x,h1,h2){
a<-mavback(x,h1)
b<-mavback(x,h1-h2)
c<-(1/h2)*(h1*a -(h1-h2)*b)
return(c)
}
```

Moving Averages · UC Business Analytics R Programming Guide, Moving averages is a smoothing approach that averages values from a window of illustrate trends with a 3 year moving average rather than a 1 year moving average. we can predict the value for t+1 by averaging over k periods prior to t+​1. To the best of my knowledge, R does not have a built-in function to calculate moving averages. Using the filter function, however, we can write a short function for moving averages: mav <- function(x,n=5){stats::filter(x,rep(1/n,n), sides=2)}

You can use `rollmean`, but set `align='right'`. Or you could use `rollmeanr`, which has `align='right'` as the default.

```ma3 <- rollmeanr(x[,1],3,fill=NA)
```

...but you would still need to lag the result. Another solution is to use `rollapply` with a list for the `width` argument:

```ma3 <- rollapplyr(x[,1],list(-(3:1)),mean,fill=NA)
```

Calculating a moving average, Plot the unsmoothed data (gray) plot(x, y, type="l", col=grey(.5)) # Draw gridlines grid() # Smoothed with lag: # average of current sample and 19 previous  To make the moving average symmetric (and therefore more accurate), we then take a 2-MA of the 4-MA to create a 2 x 4-MA. For the 2-MA step, we average the current and previous moving averages, thus resulting in an overall estimate of: This two-step process can be performed easily with the ma function by setting order = 4 and centre = TRUE.

A simplier implementation of w_i_l_l's mavback function based on his mav function

`mavback <- function(x,n){ filter(x, c(0, rep(1/n,n)), sides=1) }`

movavg function, Different types of moving average of a time series. calculated by adding the new value and subtracting the last average from the resulting sum. col = 2) # y <- movavg(abbshares, 50, "t"); lines(y, col = 3) # y <- movavg(abbshares, 50, "w");​  Suppose your data is a noisy sine wave with some missing values: set.seed ( 993 ) x <- 1 : 300 y <- sin ( x / 20 ) + rnorm ( 300 , sd = .1 ) y [ 251 : 255 ] <- NA The filter() function can be used to calculate a moving average.

ma function, ma computes a simple moving average smoother of a given time series. As neighbouring observations of a time series are likely to be similar in value, Documentation reproduced from package forecast, version 8.12, License: GPL-3​  The first modified moving average is calculated like a simple moving average. Subsequent values are calculated by adding the new value and subtracting the last average from the resulting sum. e for``exponential", it computes the exponentially weighted moving average. The exponential moving average is a weighted moving average that reduces

R: Calculating rolling or moving averages, Here I was trying to do a rolling average which took into account the last 3 numbers so I expected to get just two numbers back – 4.333333 and  The values in the last column are obtained by taking a moving average of order 2 of the values in the previous column. For example, the first two values in the 4-MA column are 451.25=(443+410+420+532)/4 and 448.75=(410+420+532+433)/4. The first value in the 2x4-MA column is the average of these two: 450.00=(451.25+448.75)/2.

Microsoft Azure Machine Learning, For each value, its moving average is the average of the previous three values Moving Average = Sum of previous 3 values/3 [146 ] Extensibility with R and  In the moving average process, the current value of the time series X t is expressed linearly in terms of current and previous values of a white noise series ε t,ε t−1,… . This noise series is constructed from the forecast errors or residuals when load observations become available.