movstd

R2026b

Moving standard deviation

Syntax

S = movstd(A,k)

S = movstd(A,[kb kf])

S = movstd(___,w)

S = movstd(___,w,dim)

S = movstd(___,nanflag)

S = movstd(___,Name=Value)

[S,mu] = movstd(___)

Description

S = movstd(A,k) returns the local k-point standard deviation values. Each standard deviation is calculated over a sliding window of length k across neighboring elements in A. When k is odd, the window is centered about the element in the current position. When k is even, the window is centered about the current and previous elements. The window size is automatically truncated at the endpoints when there are not enough elements to fill the window. When the window is truncated, the standard deviation is calculated over only the elements that fill the window. S is the same size as A.

• If A is a vector, then movstd operates along the length of the vector A.

• If A is a multidimensional array, then movstd operates along the first dimension of A whose size is greater than 1.

• If A is a table or timetable, then movstd operates along the variables of A. (since R2025a)

S = movstd(A,[kb kf]) computes the standard deviation with a window of length kb+kf+1. The calculation includes the element in the current position, the preceding kb elements, and the following kf elements..

S = movstd(___,w) specifies a normalization factor for any of the previous syntaxes. When w = 0 (default), S is normalized by k-1 for window length k. When w = 1, S is normalized by k.

S = movstd(___,w,dim) specifies the dimension of A to operate along for any of the previous syntaxes. Always specify the weight w from the previous syntax when specifying dim. For example, movstd(A,k,0,2) operates along the columns of a matrix A, computing the k-element sliding standard deviation for each row. The normalization factor is the default, k-1.

S = movstd(___,nanflag) specifies whether to include or omit NaN values in A. For example, movstd(A,k,"omitnan") ignores NaN values when computing each standard deviation. By default, movstd includes NaN values.

S = movstd(___,Name=Value) specifies additional options for the standard deviation using one or more name-value arguments. For example, if x is a time vector, then movstd(A,k,SamplePoints=x) computes the moving standard deviation relative to the times in x.

[S,mu] = movstd(___) also returns the moving mean of the elements in A used to calculate the moving standard deviation.

Examples

Centered Moving Standard Deviation of Vector

Compute the three-point centered moving standard deviation of a row vector. When there are fewer than three elements in the window at the endpoints, take the standard deviation over the elements that are available.

A = [4 8 6 -1 -2 -3 -1 3 4 5];
S = movstd(A,3)

S = 1×10

    2.8284    2.0000    4.7258    4.3589    1.0000    1.0000    3.0551    2.6458    1.0000    0.7071

Trailing Moving Standard Deviation of Vector

Compute the three-point trailing moving standard deviation of a row vector. When there are fewer than three elements in the window at the endpoints, take the standard deviation over the elements that are available.

A = [4 8 6 -1 -2 -3 -1 3 4 5];
S = movstd(A,[2 0])

S = 1×10

         0    2.8284    2.0000    4.7258    4.3589    1.0000    1.0000    3.0551    2.6458    1.0000

Specify Normalization for Moving Standard Deviation

Compute the three-point centered moving standard deviation of a row vector and normalize each standard deviation by the number of elements in the window.

A = [4 8 6 -1 -2 -3 -1 3 4 5];
S = movstd(A,3,1)

S = 1×10

    2.0000    1.6330    3.8586    3.5590    0.8165    0.8165    2.4944    2.1602    0.8165    0.5000

Moving Standard Deviation of Matrix

Compute the three-point centered moving standard deviation for each row of a matrix. The window starts on the first row, slides horizontally to the end of the row, then moves to the second row, and so on. The dimension argument is two, which slides the window across the columns of A. Always specify the normalization factor when specifying the dimension.

A = [4 8 6; -1 -2 -3; -1 3 4];
S = movstd(A,3,0,2)

S = 3×3

    2.8284    2.0000    1.4142
    0.7071    1.0000    0.7071
    2.8284    2.6458    0.7071

Moving Standard Deviation Excluding Missing Values

Create a row vector containing NaN values.

A = [4 8 NaN -1 -2 -3 NaN 3 4 5];

Compute the three-point centered moving standard deviation of the vector, excluding NaN values. For windows that contain any NaN value, movstd computes with the non-NaN elements.

S = movstd(A,3,"omitnan")

S = 1×10

    2.8284    2.8284    6.3640    0.7071    1.0000    0.7071    4.2426    0.7071    1.0000    0.7071

Sample Points for Moving Standard Deviation

Compute a 3-hour centered moving standard deviation of the data in A according to the time vector t.

A = [4 8 6 -1 -2 -3];
k = hours(3);
t = datetime(2016,1,1,0,0,0) + hours(0:5)

t = 1×6 datetime
   01-Jan-2016 00:00:00   01-Jan-2016 01:00:00   01-Jan-2016 02:00:00   01-Jan-2016 03:00:00   01-Jan-2016 04:00:00   01-Jan-2016 05:00:00

S = movstd(A,k,SamplePoints=t)

S = 1×6

    2.8284    2.0000    4.7258    4.3589    1.0000    0.7071

Return Only Full-Window Standard Deviations

Compute the three-point centered moving standard deviation of a row vector, but discard any calculation that uses fewer than three points from the output. In other words, return only the standard deviations computed from a full three-element window, discarding endpoint calculations.

A = [4 8 6 -1 -2 -3 -1 3 4 5];
S = movstd(A,3,Endpoints="discard")

S = 1×8

    2.0000    4.7258    4.3589    1.0000    1.0000    3.0551    2.6458    1.0000

Moving Standard Deviations of Table

Create a table from the columns of a magic square.

A = magic(3);
T = array2table(A)

T = 3×3 table
    A1    A2    A3
    __    __    __

    8     1     6 
    3     5     7 
    4     9     2 

Calculate the moving standard deviations of the table variables using movstd.

S = movstd(T,3)

S = 3×3 table
      A1         A2        A3   
    _______    ______    _______

     3.5355    2.8284    0.70711
     2.6458         4     2.6458
    0.70711    2.8284     3.5355

To calculate moving standard deviations only for specified variables, specify the DataVariables name-value argument.

S2 = movstd(T,3,DataVariables=["A2" "A3"])

S2 = 3×3 table
    A1      A2        A3   
    __    ______    _______

    8     2.8284    0.70711
    3          4     2.6458
    4     2.8284     3.5355

To append the moving standard deviations to the table instead of replacing values, specify ReplaceValues as false.

S3 = movstd(T,3,ReplaceValues=false)

S3 = 3×6 table
    A1    A2    A3    A1_movstd    A2_movstd    A3_movstd
    __    __    __    _________    _________    _________

    8     1     6       3.5355      2.8284       0.70711 
    3     5     7       2.6458           4        2.6458 
    4     9     2      0.70711      2.8284        3.5355 

Return Moving Mean

Compute the moving standard deviation and moving mean of a vector. Each element in S is the standard deviation of a sliding window, and each element in mu is the mean of the same window.

A = [4 8 6 -1 -2 -3 -1 3 4 5];
[S,mu] = movstd(A,3)

S = 1×10

    2.8284    2.0000    4.7258    4.3589    1.0000    1.0000    3.0551    2.6458    1.0000    0.7071

mu = 1×10

    6.0000    6.0000    4.3333    1.0000   -2.0000   -2.0000   -0.3333    2.0000    4.0000    4.5000

Input Arguments

A — Input data

vector | matrix | multidimensional array | table | timetable

Input data, specified as a vector, matrix, multidimensional array, table, or timetable.

Data Types: single | double | logical | table | timetable

k — Window length

numeric or duration scalar

Window length, specified as a numeric or duration scalar. When k is a positive integer scalar, the centered standard deviation includes the element in the current position plus surrounding neighbors.

For example, M = movstd(A,3) computes an array of local three-point standard deviation values.

[kb kf] — Directional window length

numeric or duration two-element vector

Directional window length, specified as a numeric or duration two-element vector. When kb and kf are nonnegative integer scalars, the calculation is over kb+kf+1 elements. The calculation includes the element in the current position, kb elements before the current position, and kf elements after the current position.

For example, movstd(A,[2 1]) computes an array of local four-point standard deviation values.

w — Weight

0 (default) | 1

Weight, specified as one of these values:

• 0 — Normalize by k-1, where k is the window length. If k=1, the weight is k.

• 1 — Normalize by k.

Data Types: single | double

dim — Dimension to operate along

positive integer scalar

Dimension to operate along, specified as a positive integer scalar. If you do not specify the dimension, then the default is the first array dimension whose size does not equal 1.

Dimension dim indicates the dimension that movstd operates along, that is, the direction in which the specified window slides.

Consider an m-by-n input matrix, A:

• movstd(A,k,0,1) computes the k-element sliding standard deviation for each column of A and returns an m-by-n matrix.

  

• movstd(A,k,0,2) computes the k-element sliding standard deviation for each row of A and returns an m-by-n matrix.

  

If A is a table or timetable, then you cannot specify dim. The movstd function always operates along the variables of tables and timetables. (since R2025a)

nanflag — Missing value condition

"includemissing" (default) | "includenan" | "omitmissing" | "omitnan"

Missing value condition, specified as one of these values:

• "includemissing" or "includenan" — Include NaN values in A when computing each standard deviation. If any element in the window is NaN, then the corresponding element in S is NaN. "includemissing" and "includenan" have the same behavior.

• "omitmissing" or "omitnan" — Ignore NaN values in A, and compute each standard deviation over fewer points. If all elements in the window are NaN, then the corresponding element in S is NaN. "omitmissing" and "omitnan" have the same behavior.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: S = movstd(A,k,Endpoints="fill")

Endpoints — Method to treat windows near endpoints

"shrink" (default) | "discard" | "fill" | numeric or logical scalar

Method to treat windows near endpoints, specified as one of these options:

Value	Description
"shrink"	Shrink the window size near the endpoints of the input to include only existing elements.
"discard"	Do not output any standard deviations values when the window does not completely overlap with existing elements. If A is a table or timetable, then you cannot specify Endpoints as "discard".
"fill"	Replace nonexisting elements with NaN.
numeric or logical scalar	Replace nonexisting elements with the specified numeric or logical value.

SamplePoints — Sample points for computing standard deviations

vector

Sample points for computing standard deviations, specified as a vector. The sample points represent the locations of the data in A. Sample points do not need to be uniformly sampled. By default, the sample points vector is [1 2 3 ... ].

Moving windows are defined relative to the sample points, which must be sorted and contain unique elements. For example, if t is a vector of times corresponding to the input data, then movstd(rand(1,10),3,SamplePoints=t) has a window that represents the time interval between t(i)-1.5 and t(i)+1.5.

When the sample points vector has data type datetime or duration, then the moving window length must have type duration.

If the sample points are nonuniformly spaced and Endpoints is specified, then its value must be "shrink".

DataVariables — Table or timetable variables to operate on

table or timetable variable name | scalar | vector | cell array | pattern | function handle | table vartype subscript

Since R2025a

Table or timetable variables to operate on, specified as one of the options in this table.

• If you do not specify DataVariables, then movstd operates on all variables. This behavior is the default behavior.

• If you specify DataVariables, then movstd operates only on the specified variables. Other variables not specified by DataVariables pass through to the output without modification.

Indexing Scheme	Values to Specify	Examples
Variable name	A string scalar or character vector A string array or cell array of character vectors A pattern object	"A" or 'A' — A variable named A ["A" "B"] or {'A','B'} — Two variables named A and B "Var"+digitsPattern(1) — Variables named "Var" followed by a single digit
Variable index	An index number that refers to the location of a variable in the table A vector of numbers A logical vector. Typically, this vector is the same length as the number of variables, but you can omit trailing 0 (false) values.	3 — The third variable from the table [2 3] — The second and third variables from the table [false false true] — The third variable
Function handle	A function handle that takes a table variable as input and returns a logical scalar	@isnumeric — All the variables containing numeric values
Variable type	A vartype subscript that selects variables of a specified type	vartype("numeric") — All the variables containing numeric values

ReplaceValues — Replace values indicator

true or 1 (default) | false or 0

Since R2025a

Replace values indicator, specified as one of these values when A is a table or timetable.

• true or 1 — In the output table or timetable, replace input table or timetable variables with variables containing output values from movstd.

• false or 0 — Append variables containing output values from movstd to the output table or timetable.

For vector, matrix, or multidimensional array input data, ReplaceValues is not supported.

Output Arguments

S — Moving standard deviation

vector | matrix | multidimensional array | table | timetable

Moving standard deviation, returned as a vector, matrix, multidimensional array, table, or timetable. S is the same size as the input A.

mu — Moving mean

vector | matrix | multidimensional array | table | timetable

Since R2026b

Moving mean, returned as a vector, matrix, multidimensional array, table, or timetable. mu is the same size as the input A.

More About

Standard Deviation

For a finite-length vector A made up of N scalar observations, the standard deviation is defined as

$$\begin{array}{l}S={\sqrt{\frac{1}{N-1}{\displaystyle \sum _{i=1}^N|A_i-\mu {|}^2}}}^{},\hfill \\ \hfill \end{array}$$

where μ is the mean of A:

$$\mu =\frac{1}{N}{\displaystyle \sum _{i=1}^N{A}_i}.$$

The standard deviation is the square root of the variance. Some definitions of standard deviation use a normalization factor of N instead of N-1, which you can specify by setting w to 1.

Extended Capabilities

Tall Arrays

Calculate with arrays that have more rows than fit in memory.

The movstd function supports tall arrays with the following usage notes and limitations:

• The SamplePoints name-value argument is not supported.

• Tall timetables are not supported.

• The second output argument mu is not supported. (since R2026b)

For more information, see Tall Arrays.

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• The DataVariables and ReplaceValues name-value arguments are not supported.

• Table and timetable inputs are not supported.

• If you disable dynamic memory allocation, the window length k or [kb kf] must be a constant.

• Returning the moving mean is not supported. (since R2026b)

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Refer to the usage notes and limitations in the C/C++ Code Generation section. The same usage notes and limitations apply to GPU code generation.

Thread-Based Environment

Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

The movstd function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

The movstd function supports GPU array input with these usage notes and limitations:

• The SamplePoints, DataVariables, and ReplaceValues name-value arguments are not supported.

• Table and timetable inputs are not supported.

• Returning the moving mean is not supported. (since R2026b)

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Distributed Arrays

Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

The movstd function supports distributed arrays with these usage notes and limitations:

• Returning the moving mean is not supported. (since R2026b)

For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).

Version History

Introduced in R2016a

R2026b: Return moving mean

Return the moving mean used to calculate the moving standard deviation by specifying a second output argument.

R2025a: Calculate moving statistics for data in tables and timetables

You can specify tables and timetables as inputs containing numeric data to operate on. By default, movstd operates on all variables in a table or timetable. However, you can specify which variables to operate on. You can also append output values from movstd as new variables in the output table or timetable.

R2023a: Specify missing value condition

Include or omit missing values in the input array when computing each standard deviation by using the "includemissing" or "omitmissing" options. These options have the same behavior as the "includenan" and "omitnan" options, respectively.

R2023a: Improved performance when computing over matrix with sample points

The movstd function shows improved performance when computing over a matrix when there are sample points.

For example, this code computes the moving standard deviation of a 300-by-300 matrix with corresponding sample points. The code is about 2.3x faster than in the previous release.

function timingMovstd
A = randn(300);
t = sort(rand(300,1));
tic
for k = 1:2000
    movstd(A,0.1,"SamplePoints",t);
end
toc
end

The approximate execution times are:

R2022b: 1.49 s

R2023a: 0.64 s

The code was timed on a Windows^® 10, Intel^® Xeon^® CPU E5-1650 v4 @ 3.60 GHz test system by calling the timingMovstd function.