tzoffset

Time zone offset from UTC

Syntax

dt = tzoffset(t)

[dt,dst] = tzoffset(t)

Description

dt = tzoffset(t) returns an array of durations equal to the time zone offset from UTC of each datetime value in t. That is, dt is the amount of time that each datetime in t differs from UTC. For datetimes that occur during Daylight Saving Time (DST), dt includes the time shift for DST. The offset for unzoned datetime values is not defined.

[dt,dst] = tzoffset(t) additionally returns the time shift for Daylight Saving Time for each datetime in t.

Examples

Find Time Zone Offset

Create a datetime array in the time zone for New York City.

t1 = datetime('today','TimeZone','America/New_York');
t = dateshift(t1,'end','month',[1:3:9])

t = 1x3 datetime
   31-Oct-2024   31-Jan-2025   30-Apr-2025

Find the time zone offset from UTC and the time shift for Daylight Saving Time for each datetime value.

[dt,dst] = tzoffset(t)

dt = 1x3 duration
   -04:00   -05:00   -04:00

dst = 1x3 duration
   01:00   00:00   01:00

Input Arguments

t — Input date and time

datetime array

Input date and time, specified as a datetime array.

Output Arguments

dt — Time zone offset from UTC

scalar | vector | matrix | multidimensional array

Time zone offset from UTC, returned as a scalar, vector, matrix, or multidimensional duration array. dt is the same size as t.

dst — Time shift for Daylight Saving Time

scalar | vector | matrix | multidimensional array

Time shift for Daylight Saving Time, returned as a scalar, vector, matrix, or multidimensional duration array. dt is the same size as t. A value of 01:00 indicates that the corresponding datetime in t occurs during Daylight Saving Time in a location that observes it. For locations that do not observe Daylight Saving Time, the elements of the dst array are all 00:00.

Extended Capabilities

Tall Arrays

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

The tzoffset function fully supports tall arrays. For more information, see Tall Arrays.

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.

Distributed Arrays

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

This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).

Version History

Introduced in R2014b