fillmissing

Fill missing values

Syntax

F = fillmissing(A,'constant',v)

F = fillmissing(A,method)

F = fillmissing(A,movmethod,window)

F = fillmissing(A,fillfun,gapwindow)

F = fillmissing(___,dim)

F = fillmissing(___,Name,Value)

[F,TF] = fillmissing(___)

Description

F = fillmissing(A,'constant',v) fills missing entries of an array or table with the constant value v. If A is a matrix or multidimensional array, then v can be either a scalar or a vector. When v is a vector, each element specifies the fill value in the corresponding column of A. If A is a table or timetable, then v can also be a cell array whose elements contain fill values for each table variable.

Missing values are defined according to the data type of A:

• NaN — double, single, duration, and calendarDuration

• NaT — datetime

• <missing> — string

• <undefined> — categorical

• ' ' — char

• {''} — cell of character arrays

If A is a table, then the data type of each column defines the missing value for that column.

F = fillmissing(A,method) fills missing entries using the method specified by method. For example, fillmissing(A,'previous') fills missing entries with the previous non-missing entry of A.

F = fillmissing(A,movmethod,window) fills missing entries using a moving window mean or median with window length window. For example, fillmissing(A,'movmean',5) fills data with a moving average using a window length of 5.

F = fillmissing(A,fillfun,gapwindow) fills gaps of missing entries using a custom method specified by a function handle fillfun and a fixed window surrounding each gap from which the fill values are computed. fillfun must have the input arguments xs, ts, and tq, which are vectors containing the sample data xs of length gapwindow, the sample data locations ts of length gapwindow, and the missing data locations tq. The locations in ts and tq are a subset of the sample points vector.

F = fillmissing(___,dim) specifies the dimension of A to operate along. By default, fillmissing operates along the first dimension whose size does not equal 1. For example, if A is a matrix, then fillmissing(A,2) operates across the columns of A, filling missing data row by row.

F = fillmissing(___,Name,Value) specifies additional parameters for filling missing values using one or more name-value pair arguments. For example, if t is a vector of time values, then fillmissing(A,'linear','SamplePoints',t) interpolates the data in A relative to the times in t.

[F,TF] = fillmissing(___) also returns a logical array corresponding to the entries of A that were filled.

Examples

Vector with NaN Values

Create a vector that contains NaN values and replace each NaN with the previous non-missing value.

A = [1 3 NaN 4 NaN NaN 5];
F = fillmissing(A,'previous')

F = 1×7

     1     3     3     4     4     4     5

Matrix with NaN Values

Create a 2-by-2 matrix with a NaN value in each column. Fill NaN with 100 in the first column and 1000 in the second column.

A = [1 NaN; NaN 2]

A = 2×2

     1   NaN
   NaN     2

F = fillmissing(A,'constant',[100 1000])

F = 2×2

           1        1000
         100           2

Interpolate Missing Data

Use interpolation to replace NaN values in non-uniformly sampled data.

Define a vector of non-uniform sample points and evaluate the sine function over the points.

x = [-4*pi:0.1:0, 0.1:0.2:4*pi];
A = sin(x);

Inject NaN values into A.

A(A < 0.75 & A > 0.5) = NaN;

Fill the missing data using linear interpolation, and return the filled vector F and the logical vector TF. The value 1 (true) in entries of TF corresponds to the values of F that were filled.

[F,TF] = fillmissing(A,'linear','SamplePoints',x);

Plot the original data and filled data.

plot(x,A,'.', x(TF),F(TF),'o')
xlabel('x');
ylabel('sin(x)')
legend('Original Data','Filled Missing Data')

Replace NaN with Moving Median

Use a moving median to fill missing numeric data.

Create a vector of sample points x and a vector of data A that contains missing values.

x = linspace(0,10,200); 
A = sin(x) + 0.5*(rand(size(x))-0.5); 
A([1:10 randi([1 length(x)],1,50)]) = NaN; 

Replace NaN values in A using a moving median with a window of length 10, and plot both the original data and the filled data.

F = fillmissing(A,'movmedian',10);  
plot(x,F,'r.-',x,A,'b.-') 
legend('Filled Missing Data','Original Data')

Fill with Previous Value Using Custom Function

Define a custom function to fill NaN values with the previous nonmissing value.

Define a vector of sample points t and a vector of corresponding data A containing NaN values. Plot the data.

t = 10:10:100;
A = [0.1 0.2 0.3 NaN NaN 0.6 0.7 NaN 0.9 1];
plot(t,A,'o')

Use the local function forwardfill (defined at the end of the example) to fill missing gaps with the previous nonmissing value. The function handle inputs include:

• xs — data values used for filling

• ts — locations of the values used for filling relative to the sample points

• tq — locations of the missing values relative to the sample points

• n — number of values in the gap to fill

n = 2;
gapwindow = [10 0];

[F,TF] = fillmissing(A,@(xs,ts,tq) forwardfill(xs,ts,tq,n),gapwindow,'SamplePoints',t);

The gap window value [10 0] tells fillmissing to consider one data point before a missing gap and no data points after a gap, since the previous nonmissing value is located 10 units prior to the gap. The function handle input values determined by fillmissing for the first gap are:

• xs = 0.3

• ts = 30

• tq = [40 50]

The function handle input values for the second gap are:

• xs = 0.7

• ts = 70

• tq = 80

Plot the original data and the filled data.

plot(t,A,'o',t(TF),F(TF),'ro')

function y = forwardfill(xs,ts,tq,n)
% Fill n values in the missing gap using the previous nonmissing value
y = NaN(1,numel(tq));
y(1:min(numel(tq),n)) = xs;
end

Matrix with Missing Endpoints

Create a matrix with missing entries and fill across the columns (second dimension) one row at a time using linear interpolation. For each row, fill leading and trailing missing values with the nearest non-missing value in that row.

A = [NaN NaN 5 3 NaN 5 7 NaN 9 NaN;
     8 9 NaN 1 4 5 NaN 5 NaN 5;
     NaN 4 9 8 7 2 4 1 1 NaN]

A = 3×10

   NaN   NaN     5     3   NaN     5     7   NaN     9   NaN
     8     9   NaN     1     4     5   NaN     5   NaN     5
   NaN     4     9     8     7     2     4     1     1   NaN

F = fillmissing(A,'linear',2,'EndValues','nearest')

F = 3×10

     5     5     5     3     4     5     7     8     9     9
     8     9     5     1     4     5     5     5     5     5
     4     4     9     8     7     2     4     1     1     1

Table with Multiple Data Types

Fill missing values for table variables with different data types.

Create a table whose variables include categorical, double, and char data types.

A = table(categorical({'Sunny';'Cloudy';''}),[66;NaN;54],{'';'N';'Y'},[37;39;NaN],...
    'VariableNames',{'Description' 'Temperature' 'Rain' 'Humidity'})

A=3×4 table
    Description    Temperature       Rain       Humidity
    ___________    ___________    __________    ________

    Sunny               66        {0x0 char}       37   
    Cloudy             NaN        {'N'     }       39   
    <undefined>         54        {'Y'     }      NaN   

Replace all missing entries with the value from the previous entry. Since there is no previous element in the Rain variable, the missing character vector is not replaced.

F = fillmissing(A,'previous')

F=3×4 table
    Description    Temperature       Rain       Humidity
    ___________    ___________    __________    ________

      Sunny            66         {0x0 char}       37   
      Cloudy           66         {'N'     }       39   
      Cloudy           54         {'Y'     }       39   

Replace the NaN values from the Temperature and Humidity variables in A with 0.

F = fillmissing(A,'constant',0,'DataVariables',{'Temperature','Humidity'})

F=3×4 table
    Description    Temperature       Rain       Humidity
    ___________    ___________    __________    ________

    Sunny              66         {0x0 char}       37   
    Cloudy              0         {'N'     }       39   
    <undefined>        54         {'Y'     }        0   

Alternatively, use the isnumeric function to identify the numeric variables to operate on.

F = fillmissing(A,'constant',0,'DataVariables',@isnumeric)

F=3×4 table
    Description    Temperature       Rain       Humidity
    ___________    ___________    __________    ________

    Sunny              66         {0x0 char}       37   
    Cloudy              0         {'N'     }       39   
    <undefined>        54         {'Y'     }        0   

Now fill the missing values in A with a specified constant for each table variable, which are contained in a cell array.

F = fillmissing(A,'constant',{categorical({'None'}),1000,'Unknown',1000})

F=3×4 table
    Description    Temperature       Rain        Humidity
    ___________    ___________    ___________    ________

      Sunny             66        {'Unknown'}        37  
      Cloudy          1000        {'N'      }        39  
      None              54        {'Y'      }      1000  

Specify Maximum Gap

Create a time vector t in seconds and a corresponding vector of data A that contains NaN values.

t = seconds([2 4 8 17 98 134 256 311 1001]);
A = [1 3 23 NaN NaN NaN 100 NaN 233];

Fill only missing values in A that correspond to a maximum gap size of 250 seconds. Since the second gap is larger than 250 seconds, the NaN value is not filled.

F = fillmissing(A,'linear','SamplePoints',t,'MaxGap',seconds(250))

F = 1×9

    1.0000    3.0000   23.0000   25.7944   50.9435   62.1210  100.0000       NaN  233.0000

Input Arguments

A — Input data

vector | matrix | multidimensional array | table | timetable

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

When the input argument is a cell array, it must be a cell array of character vectors. If A is a timetable, then only table values are filled. If the associated vector of row times contains a NaT or NaN value, then fillmissing produces an error. Row times must be unique and listed in ascending order.

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | char | string | cell | table | timetable | categorical | datetime | duration | calendarDuration

v — Fill constant

scalar | vector | cell array

Fill constant, specified as a scalar, vector, or cell array.

v can be a vector when A is a matrix or multidimensional array, indicating a different fill value for each operating dimension. The length of v must match the length of the operating dimension.

v can be a cell array of fill values when A is a table or timetable, indicating a different fill value for each variable. The number of elements in the cell array must match the number of variables in the table.

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | char | cell | categorical | datetime | duration

method — Fill method

'previous' | 'next' | 'nearest' | 'linear' | 'spline' | 'pchip' | 'makima'

Fill method, specified as one of the following:

Method	Description
'previous'	previous non-missing value
'next'	next non-missing value
'nearest'	nearest non-missing value
'linear'	linear interpolation of neighboring, non-missing values (numeric, duration, and datetime data types only)
'spline'	piecewise cubic spline interpolation (numeric, duration, and datetime data types only)
'pchip'	shape-preserving piecewise cubic spline interpolation (numeric, duration, and datetime data types only)
'makima'	modified Akima cubic Hermite interpolation (numeric, duration, and datetime data types only)

movmethod — Moving method

'movmean' | 'movmedian'

Moving method to fill missing data, specified as one of the following:

Method	Description
'movmean'	Moving average over a window of length window (numeric data types only)
'movmedian'	Moving median over a window of length window (numeric data types only)

fillfun — Custom fill method

function handle

Example: @(xs,ts,tq) myfun(xs,ts,tq)

Custom fill method, specified as a function handle. Valid function handles must include the following three input arguments:

Input Argument	Description
xs	Vector containing data values used for filling. The length of xs must match the length of the specified window.
ts	Vector containing locations of the values used for filling. The length of ts must match the length of the specified window. ts is a subset of the sample points vector.
tq	Vector containing locations of the missing values. tq is a subset of the sample points vector.

The function must return either a scalar or a vector with the same length as tq.

window — Window length

positive integer scalar | two-element vector of positive integers | positive duration scalar | two-element vector of positive durations

Window length for moving methods, specified as a positive integer scalar, a two-element vector of positive integers, a positive duration scalar, or a two-element vector of positive durations. The window is defined relative to the sample points.

When window is a positive integer scalar, then the window is centered about the current element and contains window-1 neighboring elements. If window is even, then the window is centered about the current and previous elements. If window is a two-element vector of positive integers [b f], then the window contains the current element, b elements backward, and f elements forward.

When A is a timetable or 'SamplePoints' is specified as a datetime or duration vector, window must be of type duration.

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | duration

gapwindow — Gap window length

positive integer scalar | two-element vector of positive integers | positive duration scalar | two-element vector of positive durations

Gap window length for custom fill functions, specified as a positive integer scalar, a two-element vector of positive integers, a positive duration scalar, or a two-element vector of positive durations. The gap window is defined relative to the sample points.

When specifying a function handle fillfun for the fill method, the value of gapwindow represents a fixed window length that surrounds each gap of missing values in the input data. The fill value is then computed by fillfun using the values in that window. For example, for default sample points t = 1:10 and data A = [10 20 NaN NaN 50 60 70 NaN 90 100], a window length gapwindow = 3 specifies the first window as [20 NaN NaN 50] for which fillfun operates on to compute the fill value. The second gap window for which fillfun operates on is [70 NaN 90].

When A is a timetable or 'SamplePoints' is specified as a datetime or duration vector, window must be of type duration.

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | duration

dim — Dimension to operate along

positive integer scalar

Dimension to operate along, specified as a positive integer scalar. If no value is specified, then the default is the first array dimension whose size does not equal 1.

When A is a table or timetable, dim is not supported. fillmissing operates along each table or timetable variable separately.

Consider a two-dimensional input array, A.

• If dim=1, then fillmissing fills A column by column.

  

• If dim=2, then fillmissing fills A row by row.

  

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Name-Value Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: fillmissing(A,'DataVariables',{'Temperature','Altitude'}) fills only the columns corresponding to the Temperature and Altitude variables of an input table

Data Options

SamplePoints — Sample points

vector | table variable name | scalar | function handle | table vartype subscript

Sample points, specified as the comma-separated pair consisting of 'SamplePoints' and either a vector of sample point values or one of the options in the following table when the input data is a table. The sample points represent the x-axis locations of the data, and must be sorted and contain unique elements. Sample points do not need to be uniformly sampled. The vector [1 2 3 ...] is the default.

When the input data is a table, you can specify the sample points as a table variable using one of the following options.

Option for Table Input	Description	Examples
Variable name	A character vector or scalar string specifying a single table variable name	'Var1' "Var1"
Scalar variable index	A scalar table variable index	3
Logical vector	A logical vector whose elements each correspond to a table variable, where true specifies the corresponding variable as the sample points, and all other elements are false	[true false false]
Function handle	A function handle that takes a table variable as input and returns a logical scalar, which must be true for only one table variable	@isnumeric
vartype subscript	A table subscript generated by the vartype function that returns a subscript for only one variable	vartype('numeric')

Note

This name-value pair is not supported when the input data is a timetable. Timetables always use the vector of row times as the sample points. To use different sample points, you must edit the timetable so that the row times contain the desired sample points.

Moving windows are defined relative to the sample points. For example, if t is a vector of times corresponding to the input data, then fillmissing(rand(1,10),'movmean',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.

Example: fillmissing([1 NaN 3 4],'linear','SamplePoints',[1 2.5 3 4])

Example: fillmissing(T,'linear','SamplePoints',"Var1")

Data Types: single | double | datetime | duration

DataVariables — Table variables to operate on

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

Table variables to operate on, specified as the comma-separated pair consisting of 'DataVariables' and one of the options in this table. The 'DataVariables' value indicates which variables of the input table to fill. Other variables in the table not specified by 'DataVariables' pass through to the output without being operated on.

Option	Description	Examples
Variable name	A character vector or scalar string specifying a single table variable name	'Var1' "Var1"
Vector of variable names	A cell array of character vectors or string array where each element is a table variable name	{'Var1' 'Var2'} ["Var1" "Var2"]
Scalar or vector of variable indices	A scalar or vector of table variable indices	1 [1 3 5]
Logical vector	A logical vector whose elements each correspond to a table variable, where true includes the corresponding variable and false excludes it	[true false true]
Function handle	A function handle that takes a table variable as input and returns a logical scalar	@isnumeric
vartype subscript	A table subscript generated by the vartype function	vartype('numeric')

Example: fillmissing(T,'linear','DataVariables',["Var1" "Var2" "Var4"])

Missing Value Options

EndValues — Method for handling endpoints

'extrap' (default) | 'previous' | 'next' | 'nearest' | 'none' | scalar

Method for handling endpoints, specified as the comma-separated pair consisting of 'EndValues' and one of 'extrap', 'previous', 'next', 'nearest', 'none', or a constant scalar value. The endpoint fill method handles leading and trailing missing values based on the following definitions:

Method	Description
'extrap'	same as method
'previous'	previous non-missing value
'next'	next non-missing value
'nearest'	nearest non-missing value
'none'	no fill value
scalar	constant value (numeric, duration, and datetime data types only)

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | datetime | duration

MissingLocations — Known missing indicator

vector | matrix | multidimensional array

Known missing indicator, specified as the comma-separated pair consisting of 'MissingLocations' and a logical vector, matrix, or multidimensional array of the same size as A. The indicator elements can be true to indicate a missing value in the corresponding location of A or false otherwise.

Data Types: logical

MaxGap — Maximum gap size to fill

numeric scalar | duration scalar | calendarDuration scalar

Maximum gap size to fill, specified as a numeric scalar, duration scalar, or calendarDuration scalar. Gaps are clusters of consecutive missing values whose size is the distance between the non-missing values surrounding the gap. The gap size is computed relative to the sample points. Gaps smaller than or equal to the max gap size are filled, and gaps larger than the gap size are not.

For example, consider the vector y = [25 NaN NaN 100] using the default sample points [1 2 3 4]. The gap size in the vector is computed from the sample points as 4 - 1 = 3, so a MaxGap value of 2 leaves the missing values unaltered, while a MaxGap value of 3 fills in the missing values.

For missing values at the beginning or end of the data:

• A single missing value at the end of the input data has a gap size of 0 and is always filled.

• Clusters of missing values occurring at the beginning or end of the input data are not completely surrounded by non-missing values, so the gap size is computed using the nearest existing sample points. For the default sample points 1:N, this produces a gap size that is 1 smaller than if the same cluster occurred in the middle of the data.

Output Arguments

F — Filled data

vector | matrix | multidimensional array | table | timetable

Filled data, returned as a vector, matrix, multidimensional array, table, or timetable. F is the same size as A.

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | char | string | cell | table | timetable | categorical | datetime | duration | calendarDuration

TF — Filled data indicator

vector | matrix | multidimensional array

Filled data indicator, returned as a vector, matrix, or multidimensional array. TF is a logical array where 1 (true) corresponds to entries in F that were filled and 0 (false) corresponds to unchanged entries. TF is the same size as A and F.

Data Types: logical

Extended Capabilities

Tall Arrays

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

Usage notes and limitations:

• The 'spline' and 'makima' methods are not supported.

• Function handle fill methods are not supported.

• The 'MaxGap', 'SamplePoints', and 'MissingLocations' name-value pairs are not supported.

• The 'DataVariables' name-value pair cannot specify a function handle.

• The 'EndValues' name-value pair can only specify 'extrap'.

• The syntax fillmissing(A,movmethod,window) is not supported when A is a tall timetable.

• The syntax fillmissing(A,'constant',v) must specify a scalar value for v.

• The syntax fillmissing(A,___) does not support character vector variables when A is a tall table or tall timetable.

For more information, see Tall Arrays.

C/C++ Code Generation

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

Usage notes and limitations:

• The 'MaxGap' name-value pair is not supported.

• The 'makima' option is not supported.

• When the 'SamplePoints' value has type datetime or the input data is a timetable with datetime row times, only the methods 'constant', 'movmean', and 'movmedian' are supported.

• Function handle inputs for the fillmethod argument 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.