Dates and Time

Arrays of date and time values that can be displayed in different formats

The date and time data types datetimeduration, and calendarDuration support efficient computations, comparisons, and formatted display of dates and times. Work with these arrays in the same way that you work with numeric arrays. You can add, subtract, sort, compare, concatenate, and plot date and time values. You also can represent dates and times as numeric arrays or as text. For more information, see Represent Dates and Times in MATLAB or watch Date and Time Arrays.

Functions

Create datetime Arrays

datetime Arrays that represent points in time
NaT Not-a-Time

Create duration Arrays

years Duration in years
days Duration in days
hours Duration in hours
minutes Duration in minutes
seconds Duration in seconds
milliseconds Duration in milliseconds
duration Lengths of time in fixed-length units

Create calendarDuration Arrays

calyears Calendar duration in years
calquarters Calendar duration in quarters
calmonths Calendar duration in months
calweeks Calendar duration in weeks
caldays Calendar duration in days
calendarDuration Lengths of time in variable-length calendar units

Extract Components by Time Unit

year Year number
quarter Quarter number
month Month number and name
week Week number
day Day number or name
hour Hour number
minute Minute number
second Second number

Split into Numeric Arrays

ymd Year, month, and day numbers of datetime
hms Hour, minute, and second numbers of datetime or duration
split Split calendar duration into numeric and duration units
time Convert time of calendar duration to duration
timeofday Elapsed time since midnight for datetimes
isdatetime Determine if input is datetime array
isduration Determine if input is duration array
iscalendarduration Determine if input is calendar duration array
isnat Determine NaT (Not-a-Time) elements
isdst Determine daylight saving time elements
isweekend Determine weekend elements
leapseconds List all leap seconds supported by datetime data type
timezones List time zones
tzoffset Time zone offset from UTC
between Calendar math differences
caldiff Calendar math successive differences
dateshift Shift date or generate sequence of dates and time
isbetween Determine elements within date and time interval

Convert to Numbers

datenum Convert date and time to serial date number
convertTo Convert datetime values to numeric representations
datevec Convert date and time to vector of components
exceltime Convert MATLAB datetime to Excel date number
juliandate Convert MATLAB datetime to Julian date
posixtime Convert MATLAB datetime to POSIX time
yyyymmdd Convert MATLAB datetime to YYYYMMDD numeric value
addtodate Modify date number by field

Convert to Strings

char Character array
string String array
datestr Convert date and time to string format

Current Date and Time as Numbers or String

now Current date and time as serial date number
clock Current date and time as date vector
date Current date as character vector

Day of Calendar Week or Month

calendar Calendar for specified month
eomday Last day of month
weekday Day of week

Elapsed Time as Number

etime Time elapsed between date vectors

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Machine Learning in MATLAB

Train Classification Models in Classification Learner App

Train Regression Models in Regression Learner App

Distribution Plots

Explore the Random Number Generation UI

Design of Experiments

Machine Learning Models

Logistic regression

Logistic regression create generalized linear regression model - MATLAB fitglm 2

Support Vector Machines for Binary Classification

Support Vector Machines for Binary Classification 2

Support Vector Machines for Binary Classification 3

Support Vector Machines for Binary Classification 4

Support Vector Machines for Binary Classification 5

Assess Neural Network Classifier Performance

Naive Bayes Classification

ClassificationTree class

Discriminant Analysis Classification

Ensemble classifier

ClassificationTree class 2

Train Generalized Additive Model for Binary Classification

Train Generalized Additive Model for Binary Classification 2

Classification Using Nearest Neighbors

Classification Using Nearest Neighbors 2

Classification Using Nearest Neighbors 3

Classification Using Nearest Neighbors 4

Classification Using Nearest Neighbors 5

Linear Regression

Linear Regression 2

Linear Regression 3

Linear Regression 4

Nonlinear Regression

Nonlinear Regression 2

Visualizing Multivariate Data

Generalized Linear Models

Generalized Linear Models 2

RegressionTree class

RegressionTree class 2

Neural networks

Gaussian Process Regression Models

Gaussian Process Regression Models 2

Understanding Support Vector Machine Regression

Understanding Support Vector Machine Regression 2

RegressionEnsemble



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