movsum

Moving sum

Syntax

M = movsum(A,k)

M = movsum(A,[kb kf])

M = movsum(___,dim)

M = movsum(___,nanflag)

M = movsum(___,Name,Value)

Description

M = movsum(A,k) returns the local k-point sums, where each sum is calculated over a sliding window of length k across neighboring elements of 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 sum is taken over only the elements that fill the window. M is the same size as A.

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

• If A is a multidimensional array, then movsum operates along the first dimension of A whose size does not equal 1.

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

M = movsum(A,[kb kf]) computes the sum with a window of length kb+kf+1 that includes the element in the current position, kb elements backward, and kf elements forward.

M = movsum(___,dim) specifies the dimension of A to operate along for any of the previous syntaxes. For example, if A is a matrix, then movsum(A,k,2) operates along the columns of A, computing the k-element sliding sum for each row.

M = movsum(___,nanflag) specifies whether to include or omit NaN values in A. For example, movsum(A,k,"omitnan") ignores all NaN values when computing each sum. By default, movsum includes NaN values.

M = movsum(___,Name,Value) specifies additional parameters for the sum using one or more name-value arguments. For example, if x is a time vector, then movsum(A,k,"SamplePoints",x) computes the moving sum of A relative to the times in x.

Examples

Centered Moving Sum of Vector

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

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

M = 1×10

    12    18    13     3    -6    -6    -1     6    12     9

Trailing Moving Sum of Vector

Compute the three-point trailing moving sum of a row vector. When there are fewer than three elements in the window at the endpoints, movsum takes the sum over the number of elements that are available.

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

M = 1×10

     4    12    18    13     3    -6    -6    -1     6    12

Moving Sum of Matrix

Compute the three-point centered moving sum 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.

A = [4 8 6; -1 -2 -3; -1 3 4]

A = 3×3

     4     8     6
    -1    -2    -3
    -1     3     4

M = movsum(A,3,2)

M = 3×3

    12    18    14
    -3    -6    -5
     2     6     7

Moving Sum 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 sum of the vector, excluding NaN values. For windows that contain any NaN value, movsum computes with the non-NaN elements.

M = movsum(A,3,"omitnan")

M = 1×10

    12    12     7    -3    -6    -5     0     7    12     9

Sample Points for Moving Sum

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

A = [4 8 6 -1 -2 -3];
k = hours(3);
t = datetime(16,1,1,0,0,0) + hours(0:5)

t = 1×6 datetime
   01-Jan-0016 00:00:00   01-Jan-0016 01:00:00   01-Jan-0016 02:00:00   01-Jan-0016 03:00:00   01-Jan-0016 04:00:00   01-Jan-0016 05:00:00

M = movsum(A,k,"SamplePoints",t)

M = 1×6

    12    18    13     3    -6    -5

Return Only Full-Window Sums

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

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

M = 1×8

    18    13     3    -6    -6    -1     6    12

Moving Sums 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 sums of the table variables using movsum.

M = movsum(T,3)

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

    11     6    13
    15    15    15
     7    14     9

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

M2 = movsum(T,3,DataVariables=["A2" "A3"])

M2=3×3 table
    A1    A2    A3
    __    __    __

    8      6    13
    3     15    15
    4     14     9

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

M3 = movsum(T,3,ReplaceValues=false)

M3=3×6 table
    A1    A2    A3    A1_movsum    A2_movsum    A3_movsum
    __    __    __    _________    _________    _________

    8     1     6        11            6           13    
    3     5     7        15           15           15    
    4     9     2         7           14            9    

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 | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | 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 sum includes the element in the current position plus surrounding neighbors.

For example, movsum(A,3) computes an array of local three-point sums.

[kb kf] — Directional window length

numeric or duration row vector containing two elements

Directional window length, specified as a numeric or duration row vector containing two elements. When kb and kf are positive 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, movsum(A,[2 1]) computes an array of local four-point sums.

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 movsum operates along, that is, the direction in which the specified window slides.

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

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

  

• movsum(A,k,2) computes the k-element sliding sum 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 movsum 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 sum. If any element in the window is NaN, then the corresponding element in M is NaN. "includemissing" and "includenan" have the same behavior.

• "omitmissing" or "omitnan" — Ignore NaN values in A, and compute each sum over fewer points. If all elements in the window are NaN, then the corresponding element in M is 0. "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.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: M = movsum(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 sums 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 sums

vector

Sample points for computing sums, 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 movsum(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 movsum operates on all variables. This behavior is the default behavior.

• If you specify DataVariables, then movsum 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 movsum.

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

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

Extended Capabilities

Tall Arrays

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

This function supports tall arrays with the limitations:

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

  • Table and timetable inputs are not supported.

For more information, see Tall Arrays.

C/C++ Code Generation

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

Usage notes and limitations:

• Code generation does not support sparse matrix inputs for this function.

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

• Table and timetable inputs are not supported.

Thread-Based Environment

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

This 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 movsum 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.

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™.

Usage notes and limitations:

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

• Table and timetable inputs are not supported.

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

Version History

Introduced in R2016a

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, movsum 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 movsum 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 sum 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 movsum function shows improved performance when computing over a matrix when there are sample points.

For example, this code computes the moving sum of a 300-by-300 matrix with corresponding sample points. The code is about 3x faster than in the previous release.

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

The approximate execution times are:

R2022b: 1.04 s

R2023a: 0.34

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