TemporalData
✖
TemporalData
represents temporal data with values vi at times specified by tspec.
represents a temporal data collection with values vij at times specified by tspec.
represents a temporal data collection given as lists of time-value pairs {tij,vij}.
Details and Options




- TemporalData represents a collection of paths composed of time-value pairs {tij,vij}.
- The values vij can be scalars or arrays of any dimension, but must all be of equal dimensionality.
- The following times tspec can be given:
-
Automatic use uniformly spaced times starting at 0 {tmin} use uniformly spaced times starting at tmin {tmin,tmax} use uniformly spaced times tmin to tmax {tmin,tmax,dt} use times tmin to tmax in steps of dt {{t1,t2,…}} use explicit times {t1,t2,…} {tspec1,tspec2,…} use different times for each path in the collection - The tij can be numbers or any valid input to AbsoluteTime.
- The values tmin, tmax, and dt can be given as numbers, dates, or Automatic.
- TemporalData objects of equal dimensionality {td1,td2,…} can be combined into a single object using TemporalData[{td1,td2,…}].
- Normal[td] returns a list containing time-value pairs {tij,vij} for each path.
- Specifying td[t] gives the empirical slice distribution at time t.
- A joint empirical slice distribution for times {t1,t2,…} can be obtained by specifying td[{t1,t2,…}].
- Properties of a TemporalData object td can be obtained from td["property"].
- A list of available properties can be obtained using td["Properties"].
- Some properties of the collection of paths:
-
"Components" split the collection into individual components "PathCount" the number of paths in the collection "PathLengths" a list containing the length of each path "Paths" a list containing time-value pairs {tij,vij} for each path "DatePaths" a list containing date-value pairs for each path "TimeList" a list containing times tij for each path "DateList" a list containing tij for each path as dates "ValueDimensions" the dimensionality of the values "ValueList" a list containing values vij for each path "FirstTimes" a list of first times for each path "FirstDates" a list of first times for each path as dates "LastTimes" a list of last times for each path "LastDates" a list of last times for each path as dates "FirstValues" a list of values at the first time for each path "LastValues" a list of values at the last time for each path - If dates are given as input, td["Times"] returns them in AbsoluteTime.
- Some properties for obtaining parts of the collection:
-
"Part" a subset of the original data "Path" time-value pairs {tij,vij} for a given path "DatePath" date-value pairs {dateij,vij} for a given path "PathComponents" split the multivariate path into univariate components "PathFunction" an interpolated path function "Values" values vi for a given path "Times" times ti for a given path "Dates" times ti for a given path as dates "SliceData" a slice through all paths at a given time "SliceDistribution" empirical distribution of slice data at a given time "FirstTime" the first time t1 for a given path "FirstDate" the first time t1 for a given path as date "LastTime" the last time for a given path "LastDate" the last time for a given path as date "FirstValue" the value v1 at the first time for a given path "LastValue" the value at the last time for a given path - Specifying td["Path",p] gives the time-value pairs for the paths specified by p, where p can be any valid Part specification.
- The property td["PathFunction",p] returns interpolated paths specified by p.
- Specifying td["PathComponent",p] gives the TemporalData for vector components of the values specified by p.
- Specifying td["Part",p,tspec] gives TemporalData for paths specified by p and times specified by tspec. If necessary, the paths are resampled according to "PathFunction".
- Giving td["SliceData",t] returns a slice through all paths at time t, where t can be a number or valid input to AbsoluteTime.
- The specification td["SliceData",{t1,t2,…}] gives a multivariate slice at times {t1,t2,…}.
- TemporalData takes the following options:
-
CalendarType "Gregorian" the calendar type to use HolidayCalendar {"UnitedStates","Default"} the holiday calendar to use TimeZone $TimeZone the time zone to use MetaInformation None include additional metainformation MissingDataMethod None method to use for missing values ResamplingMethod "Interpolation" the method to use for resampling paths ValueDimensions Automatic the dimensions of the values TemporalRegularity Automatic whether to assume the data is regular DateFunction Automatic how to convert dates to standard form - By default, zero-order interpolation is used for resampling paths. The setting ResamplingMethod->{"Interpolation",opts} can be given, where opts are options passed to Interpolation.
- The setting ValueDimensions->dim specifies that the values vij are of dimension dim. Setting ValueDimensions->Automatic attempts to automatically determine the dimension of the values from the data.
- Setting the MissingDataMethod->Automatic will automatically interpolate values with head Missing, according to the ResamplingMethod setting. By default, values with head Missing are treated as missing.
- Information of a TemporalData may include the following properties:
-
"DateInterval" start and end dates "DataPoints" number of data points "Regular" whether data is regularly sampled "OutputDimensions" dimensions of value output "Metadata" all metadata
Examples
open allclose allBasic Examples (3)Summary of the most common use cases
Attach temporal information to some values:

https://wolfram.com/xid/0n4lz2acfhe-byvcsb

https://wolfram.com/xid/0n4lz2acfhe-dhklb2


https://wolfram.com/xid/0n4lz2acfhe-i67wst

Create a collection of paths with equivalent times:

https://wolfram.com/xid/0n4lz2acfhe-gx0rsr

https://wolfram.com/xid/0n4lz2acfhe-f5ij1t


https://wolfram.com/xid/0n4lz2acfhe-j1kkjx

Compute the Mean and StandardDeviation at time :

https://wolfram.com/xid/0n4lz2acfhe-8e999


https://wolfram.com/xid/0n4lz2acfhe-in7m45


https://wolfram.com/xid/0n4lz2acfhe-cj9ytv

https://wolfram.com/xid/0n4lz2acfhe-oc2oy
Plot the financial time series with DateListPlot:

https://wolfram.com/xid/0n4lz2acfhe-q24mq

The value of both stocks on May 24, 2009:

https://wolfram.com/xid/0n4lz2acfhe-dckstc

The average value of each stock over the date range:

https://wolfram.com/xid/0n4lz2acfhe-g3e7kn

Scope (40)Survey of the scope of standard use cases
Basic Uses (5)
Estimate autocorrelation and partial autocorrelation for a time series:

https://wolfram.com/xid/0n4lz2acfhe-ett639

https://wolfram.com/xid/0n4lz2acfhe-di9bsl


https://wolfram.com/xid/0n4lz2acfhe-b08lh6

Generate sample paths for a random process, using RandomFunction:

https://wolfram.com/xid/0n4lz2acfhe-cih277


https://wolfram.com/xid/0n4lz2acfhe-e4ovaq

Estimate process parameters, given a sample path:

https://wolfram.com/xid/0n4lz2acfhe-1lgnt0

https://wolfram.com/xid/0n4lz2acfhe-i8umkd

Compare the CorrelationFunction for the process and data:

https://wolfram.com/xid/0n4lz2acfhe-j4nxgg

Fill in missing values in a time series:

https://wolfram.com/xid/0n4lz2acfhe-egvlih

https://wolfram.com/xid/0n4lz2acfhe-f7i96b

Use linear interpolation to fill in the missing values:

https://wolfram.com/xid/0n4lz2acfhe-fny2ce

https://wolfram.com/xid/0n4lz2acfhe-eg0ozn

Compute properties for time slices through multiple paths:

https://wolfram.com/xid/0n4lz2acfhe-sbyong

https://wolfram.com/xid/0n4lz2acfhe-02i9r2

Mean and StandardDeviation:

https://wolfram.com/xid/0n4lz2acfhe-dfd4ff

Probabilities and expectations:

https://wolfram.com/xid/0n4lz2acfhe-ezfnt8

Specifying Values and Times (19)
Give a list of values with Automatic time stamps:

https://wolfram.com/xid/0n4lz2acfhe-d6852a

https://wolfram.com/xid/0n4lz2acfhe-d2u7nb

https://wolfram.com/xid/0n4lz2acfhe-fisuci

Create a path with times starting at :

https://wolfram.com/xid/0n4lz2acfhe-gif60

https://wolfram.com/xid/0n4lz2acfhe-i30vwy

https://wolfram.com/xid/0n4lz2acfhe-bf6ge1


https://wolfram.com/xid/0n4lz2acfhe-3j8z
Dates can be given as any valid input to AbsoluteTime:

https://wolfram.com/xid/0n4lz2acfhe-e8etf2

https://wolfram.com/xid/0n4lz2acfhe-jtoc

Use equally spaced times from 10 to 50:

https://wolfram.com/xid/0n4lz2acfhe-fngsov

https://wolfram.com/xid/0n4lz2acfhe-ejvorc

https://wolfram.com/xid/0n4lz2acfhe-guhp4f


https://wolfram.com/xid/0n4lz2acfhe-g21lz

https://wolfram.com/xid/0n4lz2acfhe-b6kqwu

https://wolfram.com/xid/0n4lz2acfhe-m7k77y

Specify an Automatic endpoint:

https://wolfram.com/xid/0n4lz2acfhe-ep9ew5

https://wolfram.com/xid/0n4lz2acfhe-b0tka7

https://wolfram.com/xid/0n4lz2acfhe-f0mzk9

Extract the computed last date:

https://wolfram.com/xid/0n4lz2acfhe-u6iehi

Create a path with times 1 to 20 in steps of 2:

https://wolfram.com/xid/0n4lz2acfhe-cajxbd

https://wolfram.com/xid/0n4lz2acfhe-cudopq

https://wolfram.com/xid/0n4lz2acfhe-ef8duz

Use an Automatic endpoint and fixed step:

https://wolfram.com/xid/0n4lz2acfhe-gu5wov

https://wolfram.com/xid/0n4lz2acfhe-dlh81

https://wolfram.com/xid/0n4lz2acfhe-hr6ocm

Extract the computed last date:

https://wolfram.com/xid/0n4lz2acfhe-o5aad4

Use an Automatic start point and given frequency:

https://wolfram.com/xid/0n4lz2acfhe-fdjhz1

https://wolfram.com/xid/0n4lz2acfhe-75g8jn

https://wolfram.com/xid/0n4lz2acfhe-xg7mub

Extract the computed first time:

https://wolfram.com/xid/0n4lz2acfhe-cg5tfe

Give an explicit list of times:

https://wolfram.com/xid/0n4lz2acfhe-dd1fks

https://wolfram.com/xid/0n4lz2acfhe-civvrp

https://wolfram.com/xid/0n4lz2acfhe-g23dk3

Give an explicit list of dates:

https://wolfram.com/xid/0n4lz2acfhe-gdgis2

https://wolfram.com/xid/0n4lz2acfhe-bwz8uv

https://wolfram.com/xid/0n4lz2acfhe-ikqia

Create a collection of three paths with identical time stamps:

https://wolfram.com/xid/0n4lz2acfhe-fsc92

https://wolfram.com/xid/0n4lz2acfhe-c2qb99

https://wolfram.com/xid/0n4lz2acfhe-hw0p74

Use different time stamps for each path:

https://wolfram.com/xid/0n4lz2acfhe-cirip6

https://wolfram.com/xid/0n4lz2acfhe-cnjg2d

https://wolfram.com/xid/0n4lz2acfhe-i9s67c

https://wolfram.com/xid/0n4lz2acfhe-gickew

Create a collection of paths with different times, but the same values:

https://wolfram.com/xid/0n4lz2acfhe-gix78l

https://wolfram.com/xid/0n4lz2acfhe-fgso58

https://wolfram.com/xid/0n4lz2acfhe-h6nitw

Specify a path, using time-value pairs:

https://wolfram.com/xid/0n4lz2acfhe-k58g4i

https://wolfram.com/xid/0n4lz2acfhe-cdkmyi

https://wolfram.com/xid/0n4lz2acfhe-ic2a5k

Create a path from date-value pairs:

https://wolfram.com/xid/0n4lz2acfhe-urlgtd

https://wolfram.com/xid/0n4lz2acfhe-f5l8on


https://wolfram.com/xid/0n4lz2acfhe-3endb


https://wolfram.com/xid/0n4lz2acfhe-2xv9uj

Create a collection of paths from time-value pairs:

https://wolfram.com/xid/0n4lz2acfhe-7mlwf

https://wolfram.com/xid/0n4lz2acfhe-csac9k

https://wolfram.com/xid/0n4lz2acfhe-j79xdb

Create a collection of paths, using date-value pairs:

https://wolfram.com/xid/0n4lz2acfhe-j5lgrb

https://wolfram.com/xid/0n4lz2acfhe-xso85f

Create a path with data involving quantities:

https://wolfram.com/xid/0n4lz2acfhe-4z8db

https://wolfram.com/xid/0n4lz2acfhe-rd00x

https://wolfram.com/xid/0n4lz2acfhe-bjw1dw

Properties and Part Extraction (13)
Obtain a list of available properties:

https://wolfram.com/xid/0n4lz2acfhe-4v647o

https://wolfram.com/xid/0n4lz2acfhe-jzfxlc

Obtain properties related to the input data:

https://wolfram.com/xid/0n4lz2acfhe-b7o8w1
The number of paths in the collection:

https://wolfram.com/xid/0n4lz2acfhe-hxg1


https://wolfram.com/xid/0n4lz2acfhe-rlt5o

The values used for the first path:

https://wolfram.com/xid/0n4lz2acfhe-nrenag


https://wolfram.com/xid/0n4lz2acfhe-v2as9

Extract paths from the collection:

https://wolfram.com/xid/0n4lz2acfhe-g9gzcl

https://wolfram.com/xid/0n4lz2acfhe-zrmb0


https://wolfram.com/xid/0n4lz2acfhe-fnm723


https://wolfram.com/xid/0n4lz2acfhe-clbgdu

Show all the paths in the ensemble:

https://wolfram.com/xid/0n4lz2acfhe-hltj2y

Extract path components of vector-valued collection:

https://wolfram.com/xid/0n4lz2acfhe-ltnfr8


https://wolfram.com/xid/0n4lz2acfhe-nptv6


https://wolfram.com/xid/0n4lz2acfhe-zwj8ti


https://wolfram.com/xid/0n4lz2acfhe-oq6x84


https://wolfram.com/xid/0n4lz2acfhe-5neflg


https://wolfram.com/xid/0n4lz2acfhe-1kkj3e

Specify path and path component names and use them for extraction:

https://wolfram.com/xid/0n4lz2acfhe-vl5a45


https://wolfram.com/xid/0n4lz2acfhe-qxc5h2

Extract path component by name:

https://wolfram.com/xid/0n4lz2acfhe-17h1xu

Obtain paths as functions of time:

https://wolfram.com/xid/0n4lz2acfhe-cqgp3x

https://wolfram.com/xid/0n4lz2acfhe-bor8bx


https://wolfram.com/xid/0n4lz2acfhe-iecht


https://wolfram.com/xid/0n4lz2acfhe-6ner5n

https://wolfram.com/xid/0n4lz2acfhe-bg7yr4

Show all the paths in the collection:

https://wolfram.com/xid/0n4lz2acfhe-2bbszx

https://wolfram.com/xid/0n4lz2acfhe-egczeg

Obtain slices through the collection of paths at different time points:

https://wolfram.com/xid/0n4lz2acfhe-kgyort

https://wolfram.com/xid/0n4lz2acfhe-bclsme


https://wolfram.com/xid/0n4lz2acfhe-dmdy1u

Obtain a slice at a particular date:

https://wolfram.com/xid/0n4lz2acfhe-6e319s

https://wolfram.com/xid/0n4lz2acfhe-39hmk1

Obtain empirical slice distributions for different time points:

https://wolfram.com/xid/0n4lz2acfhe-eymzy1
An empirical slice distribution at time 0.25:

https://wolfram.com/xid/0n4lz2acfhe-bgbn7h


https://wolfram.com/xid/0n4lz2acfhe-dhwy3w

A bivariate empirical slice distribution:

https://wolfram.com/xid/0n4lz2acfhe-4wvyl


https://wolfram.com/xid/0n4lz2acfhe-9oa5c

Obtain a subset of the original data:

https://wolfram.com/xid/0n4lz2acfhe-9kke5

https://wolfram.com/xid/0n4lz2acfhe-jnklal

Take odd-numbered paths over the time range 0.2 to 0.8:

https://wolfram.com/xid/0n4lz2acfhe-n3mrl


https://wolfram.com/xid/0n4lz2acfhe-hw9yx5

Resample data over a given set of times:

https://wolfram.com/xid/0n4lz2acfhe-cm21pl

https://wolfram.com/xid/0n4lz2acfhe-bc6seq

Upsample the original path in steps of 0.25:

https://wolfram.com/xid/0n4lz2acfhe-mtwpql

The new data is sampled from the path function:

https://wolfram.com/xid/0n4lz2acfhe-if0ag0

Resample data over a given set of days:

https://wolfram.com/xid/0n4lz2acfhe-ygeegz

https://wolfram.com/xid/0n4lz2acfhe-thnnra


https://wolfram.com/xid/0n4lz2acfhe-jpc89n


https://wolfram.com/xid/0n4lz2acfhe-j2n3kh

Temporal data involving quantities:

https://wolfram.com/xid/0n4lz2acfhe-sn6ytb

https://wolfram.com/xid/0n4lz2acfhe-8uy7qh
The values are given as QuantityArray:

https://wolfram.com/xid/0n4lz2acfhe-8jcen4

Extract quantity unit information:

https://wolfram.com/xid/0n4lz2acfhe-q37i7h


https://wolfram.com/xid/0n4lz2acfhe-9acmrd

Temporal Data Arithmetic (3)
Numerical, listable functions automatically thread over values of TemporalData:

https://wolfram.com/xid/0n4lz2acfhe-j5741a

https://wolfram.com/xid/0n4lz2acfhe-cgwxa0

Compare to the result of TimeSeriesMap:

https://wolfram.com/xid/0n4lz2acfhe-kcwyr0


https://wolfram.com/xid/0n4lz2acfhe-dyeskv

Combining several TemporalData objects with identical time stamps threads over values:

https://wolfram.com/xid/0n4lz2acfhe-frgrg3


https://wolfram.com/xid/0n4lz2acfhe-bqaq9e

Create new temporal data of quantity magnitudes from existing temporal data involving quantities:

https://wolfram.com/xid/0n4lz2acfhe-ofwe4b

https://wolfram.com/xid/0n4lz2acfhe-bl5hyk

https://wolfram.com/xid/0n4lz2acfhe-znlw6o


https://wolfram.com/xid/0n4lz2acfhe-wxgead

Create new temporal data of quantity units:

https://wolfram.com/xid/0n4lz2acfhe-w2zemu


https://wolfram.com/xid/0n4lz2acfhe-5z8g34

Options (22)Common values & functionality for each option
CalendarType (1)
Specify timestamps as dates in a specific calendar using CalendarType:

https://wolfram.com/xid/0n4lz2acfhe-hjuq32

By default, the "Gregorian" calendar is being used:

https://wolfram.com/xid/0n4lz2acfhe-ufpnay

DateFunction (2)
Use DateList to define functions for interpreting ambiguous date strings:

https://wolfram.com/xid/0n4lz2acfhe-f18gx1

https://wolfram.com/xid/0n4lz2acfhe-fw45vb


https://wolfram.com/xid/0n4lz2acfhe-hl19iu


https://wolfram.com/xid/0n4lz2acfhe-h7sudg

Use DateObject to define functions for interpreting ambiguous date strings:

https://wolfram.com/xid/0n4lz2acfhe-bhtuy6

https://wolfram.com/xid/0n4lz2acfhe-obrlfy

Specify the TimeZone of the inputs:

https://wolfram.com/xid/0n4lz2acfhe-yyf3ze

HolidayCalendar (1)
Use HolidayCalendar to visualize business days in a given country:

https://wolfram.com/xid/0n4lz2acfhe-75m0wm


https://wolfram.com/xid/0n4lz2acfhe-fypm3f

MetaInformation (4)
Include additional metadata as a list of rules:

https://wolfram.com/xid/0n4lz2acfhe-kww1x

https://wolfram.com/xid/0n4lz2acfhe-lsbk46
The properties now include the metadata "Event":

https://wolfram.com/xid/0n4lz2acfhe-dz2yok

The added metadata can be used like any other property:

https://wolfram.com/xid/0n4lz2acfhe-btpnvb


https://wolfram.com/xid/0n4lz2acfhe-eax12r

Use MetaInformation to specify PlotLegends:

https://wolfram.com/xid/0n4lz2acfhe-tp68su
See the available MetaInformation:

https://wolfram.com/xid/0n4lz2acfhe-opopnd

Access specific information directly:

https://wolfram.com/xid/0n4lz2acfhe-xbxlcf


https://wolfram.com/xid/0n4lz2acfhe-590axh

Use MetaInformation to name the path components in a vector-valued TemporalData:

https://wolfram.com/xid/0n4lz2acfhe-z6yi2a


https://wolfram.com/xid/0n4lz2acfhe-td777p

Extract first and third components using either their name or number:

https://wolfram.com/xid/0n4lz2acfhe-ic4zkv

Use MetaInformation to name the paths:

https://wolfram.com/xid/0n4lz2acfhe-uh56h1

https://wolfram.com/xid/0n4lz2acfhe-j2w1yr

Extract first and third paths using either their name or number:

https://wolfram.com/xid/0n4lz2acfhe-5pu7wx

MissingDataMethod (5)
By default, values with head Missing are interpreted as missing:

https://wolfram.com/xid/0n4lz2acfhe-irdksa

https://wolfram.com/xid/0n4lz2acfhe-ewwjzm


https://wolfram.com/xid/0n4lz2acfhe-c58vln

The setting Automatic will use the ResamplingMethod setting:

https://wolfram.com/xid/0n4lz2acfhe-i2z47j

https://wolfram.com/xid/0n4lz2acfhe-f70geh


https://wolfram.com/xid/0n4lz2acfhe-e0pykq

Use cubic interpolation to interpolate the path:

https://wolfram.com/xid/0n4lz2acfhe-i9omub


https://wolfram.com/xid/0n4lz2acfhe-cj43qa

With ResamplingMethod set to None, missing values will not be interpolated:

https://wolfram.com/xid/0n4lz2acfhe-eqh7gk

https://wolfram.com/xid/0n4lz2acfhe-6wzlh


https://wolfram.com/xid/0n4lz2acfhe-jvt0d

Use a custom interpolation for filling in missing values:

https://wolfram.com/xid/0n4lz2acfhe-5ykme

https://wolfram.com/xid/0n4lz2acfhe-bb1zfn


https://wolfram.com/xid/0n4lz2acfhe-owd4j5

The method for handling missing data need not match the ResamplingMethod:

https://wolfram.com/xid/0n4lz2acfhe-bl0tz5


https://wolfram.com/xid/0n4lz2acfhe-w6lmds

Retain missing data indices as metainformation:

https://wolfram.com/xid/0n4lz2acfhe-gmd4d

https://wolfram.com/xid/0n4lz2acfhe-egrnsu


https://wolfram.com/xid/0n4lz2acfhe-bwe5cj
Highlight the interpolated regions:

https://wolfram.com/xid/0n4lz2acfhe-n1x7m4

ResamplingMethod (6)
By default, "PathFunction" gives zero-order paths that hold their value from the left:

https://wolfram.com/xid/0n4lz2acfhe-ji8clx

https://wolfram.com/xid/0n4lz2acfhe-8t86x

https://wolfram.com/xid/0n4lz2acfhe-jqqpv4

Set the InterpolationOrder to 1:

https://wolfram.com/xid/0n4lz2acfhe-isq8e

https://wolfram.com/xid/0n4lz2acfhe-cbgia2

Use zero-order interpolation that holds its value from the right:

https://wolfram.com/xid/0n4lz2acfhe-liexju

https://wolfram.com/xid/0n4lz2acfhe-dwbfn1

https://wolfram.com/xid/0n4lz2acfhe-b6dsot


https://wolfram.com/xid/0n4lz2acfhe-ii516v

https://wolfram.com/xid/0n4lz2acfhe-cop9lv

https://wolfram.com/xid/0n4lz2acfhe-k0c000

Use cubic spline interpolation for paths:

https://wolfram.com/xid/0n4lz2acfhe-gtsn0v

https://wolfram.com/xid/0n4lz2acfhe-y6n4l

https://wolfram.com/xid/0n4lz2acfhe-ghd7ry

The interpolating method used can impact the value of time slices:

https://wolfram.com/xid/0n4lz2acfhe-b3bb7w

https://wolfram.com/xid/0n4lz2acfhe-izwg6x

https://wolfram.com/xid/0n4lz2acfhe-6gf69


https://wolfram.com/xid/0n4lz2acfhe-crd7qz

Time points in between the time stamps:

https://wolfram.com/xid/0n4lz2acfhe-hqt6me


https://wolfram.com/xid/0n4lz2acfhe-jmm636

Setting the method to None will treat values not present in the data as missing:

https://wolfram.com/xid/0n4lz2acfhe-ishy32

https://wolfram.com/xid/0n4lz2acfhe-bok0dk

https://wolfram.com/xid/0n4lz2acfhe-hvkhlz


https://wolfram.com/xid/0n4lz2acfhe-n8ltur

Equivalently, use a constant Missing[]:

https://wolfram.com/xid/0n4lz2acfhe-l6oh9

https://wolfram.com/xid/0n4lz2acfhe-fim3zn

TemporalRegularity (1)
Explicitly assume that temporal data is regularly spaced:

https://wolfram.com/xid/0n4lz2acfhe-mzf2r4

https://wolfram.com/xid/0n4lz2acfhe-1ouq

https://wolfram.com/xid/0n4lz2acfhe-ztnn9l

Internal representation of times:

https://wolfram.com/xid/0n4lz2acfhe-9ppogy


https://wolfram.com/xid/0n4lz2acfhe-bm77xb

https://wolfram.com/xid/0n4lz2acfhe-cwzopx

New internal representation of times:

https://wolfram.com/xid/0n4lz2acfhe-dgxej0

TimeZone (1)
Specify the time zone of TemporalData:

https://wolfram.com/xid/0n4lz2acfhe-06gqr2

The time stamps were created in $TimeZone, but the dates are displayed in the time zone specified by the option:

https://wolfram.com/xid/0n4lz2acfhe-nvahn3

ValueDimensions (1)
By default, the dimensionality of the values is inferred from the data:

https://wolfram.com/xid/0n4lz2acfhe-i4z5rb

https://wolfram.com/xid/0n4lz2acfhe-elm7x6
The input is interpreted as 25 separate paths:

https://wolfram.com/xid/0n4lz2acfhe-6f6d0

Setting the ValueDimensions to 2 treats the data as a single path of dimension 2:

https://wolfram.com/xid/0n4lz2acfhe-cvzwt4

https://wolfram.com/xid/0n4lz2acfhe-onv1lx

Applications (2)Sample problems that can be solved with this function
Reproduce the mean function for a random process:

https://wolfram.com/xid/0n4lz2acfhe-jfiydh

https://wolfram.com/xid/0n4lz2acfhe-bdty7n

Compare the variance function for the data with the variance function for the process:

https://wolfram.com/xid/0n4lz2acfhe-etfbai

Compare the moment functions of order 4:

https://wolfram.com/xid/0n4lz2acfhe-rlusw

Estimate the variance for a slice of TransformedProcess:

https://wolfram.com/xid/0n4lz2acfhe-gh73t5

https://wolfram.com/xid/0n4lz2acfhe-j2rt9u


https://wolfram.com/xid/0n4lz2acfhe-dw892t

Compare with the actual variance:

https://wolfram.com/xid/0n4lz2acfhe-fc3k6f

Properties & Relations (4)Properties of the function, and connections to other functions
Some equivalence relationships:

https://wolfram.com/xid/0n4lz2acfhe-t57537
Two ways to extract the collection of paths:

https://wolfram.com/xid/0n4lz2acfhe-hlnper

Two ways to obtain the first path in the ensemble:

https://wolfram.com/xid/0n4lz2acfhe-djbqaq


https://wolfram.com/xid/0n4lz2acfhe-dz0pjq

TemporalData is related to TimeSeries:

https://wolfram.com/xid/0n4lz2acfhe-9rzg2

https://wolfram.com/xid/0n4lz2acfhe-fhfs8s

TimeSeries can contain only a single path:

https://wolfram.com/xid/0n4lz2acfhe-i6kbtu

The slicing behavior of TimeSeries differs from TemporalData:

https://wolfram.com/xid/0n4lz2acfhe-c2sn3y


https://wolfram.com/xid/0n4lz2acfhe-gqk8jw

TimeSeries objects can be combined into a single TemporalData object:

https://wolfram.com/xid/0n4lz2acfhe-bca8zq


https://wolfram.com/xid/0n4lz2acfhe-ijrm3

TemporalData is a generalization of EventSeries:

https://wolfram.com/xid/0n4lz2acfhe-o3eocx

https://wolfram.com/xid/0n4lz2acfhe-lzsqs

Unlike TemporalData, EventSeries does not interpolate:

https://wolfram.com/xid/0n4lz2acfhe-br0xjq


https://wolfram.com/xid/0n4lz2acfhe-gsic8t

TemporalData at a time outside the time domain extrapolates:

https://wolfram.com/xid/0n4lz2acfhe-nzwd1q


https://wolfram.com/xid/0n4lz2acfhe-meijhs

The warning message is not being issued by default but can be turned on:

https://wolfram.com/xid/0n4lz2acfhe-i5kr6k

https://wolfram.com/xid/0n4lz2acfhe-4nxc9y



https://wolfram.com/xid/0n4lz2acfhe-lo5el
Possible Issues (6)Common pitfalls and unexpected behavior
The dimensionality of the values can be ambiguous:

https://wolfram.com/xid/0n4lz2acfhe-n9djeh

https://wolfram.com/xid/0n4lz2acfhe-c2xtw5
By default, this is interpreted as an ensemble of five paths of dimension 1:

https://wolfram.com/xid/0n4lz2acfhe-f93b3u


https://wolfram.com/xid/0n4lz2acfhe-qee4ww

Setting the ValueDimensions to 2 treats the data as a single path of dimension 2:

https://wolfram.com/xid/0n4lz2acfhe-ywnnm

https://wolfram.com/xid/0n4lz2acfhe-ucu0


https://wolfram.com/xid/0n4lz2acfhe-cahfm5

Accumulating irregularly sampled temporal data:

https://wolfram.com/xid/0n4lz2acfhe-fonccy

https://wolfram.com/xid/0n4lz2acfhe-kwsr7e

Accumulate will resample to create regularly sampled temporal data:

https://wolfram.com/xid/0n4lz2acfhe-gbc5m0

Compare with accumulated values:

https://wolfram.com/xid/0n4lz2acfhe-q89a5v

To recover that behavior, assume TemporalRegularity:

https://wolfram.com/xid/0n4lz2acfhe-kinabk

Another way is to specify ResamplingMethod by setting new values to 0:

https://wolfram.com/xid/0n4lz2acfhe-jtq4zt

If the ResamplingMethod specification is not an implemented one, it will assume the value Automatic:

https://wolfram.com/xid/0n4lz2acfhe-m87sgr

https://wolfram.com/xid/0n4lz2acfhe-7qchey


https://wolfram.com/xid/0n4lz2acfhe-0va992


https://wolfram.com/xid/0n4lz2acfhe-oxljjy


Path names must be non-empty strings:

https://wolfram.com/xid/0n4lz2acfhe-y1myy4


https://wolfram.com/xid/0n4lz2acfhe-t3f2no


Path component names must be strings:

https://wolfram.com/xid/0n4lz2acfhe-llqm54

https://wolfram.com/xid/0n4lz2acfhe-d4imac


Path component names must be non-empty strings:

https://wolfram.com/xid/0n4lz2acfhe-qmu0b5

https://wolfram.com/xid/0n4lz2acfhe-zv1u0h


Temporal data with repeated component names:

https://wolfram.com/xid/0n4lz2acfhe-qw2mff


https://wolfram.com/xid/0n4lz2acfhe-8ghdnv

For a repeated name, only the first component will be repeatedly extracted:

https://wolfram.com/xid/0n4lz2acfhe-o8quhb

Use an index to access the next components with the same name:

https://wolfram.com/xid/0n4lz2acfhe-728le9

Neat Examples (1)Surprising or curious use cases
Animate the movement of the continental plates during the Mesozoic Era:

https://wolfram.com/xid/0n4lz2acfhe-v8uhp7

https://wolfram.com/xid/0n4lz2acfhe-svkicy

https://wolfram.com/xid/0n4lz2acfhe-f0rug1


https://wolfram.com/xid/0n4lz2acfhe-ysllc4

Wolfram Research (2012), TemporalData, Wolfram Language function, https://reference.wolfram.com/language/ref/TemporalData.html (updated 2019).
Text
Wolfram Research (2012), TemporalData, Wolfram Language function, https://reference.wolfram.com/language/ref/TemporalData.html (updated 2019).
Wolfram Research (2012), TemporalData, Wolfram Language function, https://reference.wolfram.com/language/ref/TemporalData.html (updated 2019).
CMS
Wolfram Language. 2012. "TemporalData." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2019. https://reference.wolfram.com/language/ref/TemporalData.html.
Wolfram Language. 2012. "TemporalData." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2019. https://reference.wolfram.com/language/ref/TemporalData.html.
APA
Wolfram Language. (2012). TemporalData. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/TemporalData.html
Wolfram Language. (2012). TemporalData. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/TemporalData.html
BibTeX
@misc{reference.wolfram_2025_temporaldata, author="Wolfram Research", title="{TemporalData}", year="2019", howpublished="\url{https://reference.wolfram.com/language/ref/TemporalData.html}", note=[Accessed: 30-March-2025
]}
BibLaTeX
@online{reference.wolfram_2025_temporaldata, organization={Wolfram Research}, title={TemporalData}, year={2019}, url={https://reference.wolfram.com/language/ref/TemporalData.html}, note=[Accessed: 30-March-2025
]}