Estimate autocorrelation and partial autocorrelation for a time series:

Generate sample paths for a random process, using RandomFunction:

Estimate process parameters, given a sample path:

Compare the CorrelationFunction for the process and data:

Fill in missing values in a time series:

Use linear interpolation to fill in the missing values:

Compute properties for time slices through multiple paths:

Mean and StandardDeviation:

Probabilities and expectations:

Give a list of values with Automatic time stamps:

Create a path with times starting at :

Use dates for starting times:

Dates can be given as any valid input to AbsoluteTime:

Use equally spaced times from 10 to 50:

Give a range of dates to use:

Specify an Automatic endpoint:

Extract the computed last date:

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

Use an Automatic endpoint and fixed step:

Extract the computed last date:

Use an Automatic start point and given frequency:

Extract the computed first time:

Give an explicit list of times:

Give an explicit list of dates:

Create a collection of three paths with identical time stamps:

Use different time stamps for each path:

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

Specify a path, using time-value pairs:

Create a path from date-value pairs:

Create a collection of paths from time-value pairs:

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

Create a path with data involving quantities:

Obtain a list of available properties:

Obtain properties related to the input data:

The number of paths in the collection:

The dimension of the values:

The values used for the first path:

The times for the first path:

Extract paths from the collection:

The first path:

Obtain the fifth path:

Plot three of the paths:

Show all the paths in the ensemble:

Extract path components of vector-valued collection:

The first component:

Obtain the second component:

Plot the paths components:

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

Extract path by name:

Extract path component by name:

Obtain paths as functions of time:

The first path function:

The 10 path function:

Plot three of the paths:

Show all the paths in the collection:

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

A slice at time :

A bivariate slice:

Obtain a slice at a particular date:

Obtain empirical slice distributions for different time points:

An empirical slice distribution at time 0.25:

A bivariate empirical slice distribution:

Obtain a subset of the original data:

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

Resample data over a given set of times:

Upsample the original path in steps of 0.25:

The new data is sampled from the path function:

Resample data over a given set of days:

Temporal data involving quantities:

The values are given as QuantityArray:

Extract quantity unit information:

Extract quantity magnitudes:

Numerical, listable functions automatically thread over values of TemporalData:

Compare to the result of TimeSeriesMap:

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

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

Create new temporal data of quantity units:

Specify timestamps as dates in a specific calendar using CalendarType:

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

Use DateList to define functions for interpreting ambiguous date strings:

Use DateObject to define functions for interpreting ambiguous date strings:

Specify the TimeZone of the inputs:

Use HolidayCalendar to visualize business days in a given country:

Include additional metadata as a list of rules:

The properties now include the metadata "Event":

The added metadata can be used like any other property:

Use MetaInformation to specify PlotLegends:

See the available MetaInformation:

Access specific information directly:

Visualize the data:

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

Extract second component:

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

Use MetaInformation to name the paths:

Extract path by name:

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

By default, values with head Missing are interpreted as missing:

The setting Automatic will use the ResamplingMethod setting:

Use cubic interpolation to interpolate the path:

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

Use a custom interpolation for filling in missing values:

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

Retain missing data indices as metainformation:

Highlight the interpolated regions:

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

Set the InterpolationOrder to 1:

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

Use a constant value:

Use cubic spline interpolation for paths:

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

Time stamps for the paths:

Time points in between the time stamps:

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

Equivalently, use a constant Missing[]:

Explicitly assume that temporal data is regularly spaced:

Internal representation of times:

New internal representation of times:

Specify the time zone of TemporalData:

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

By default, the dimensionality of the values is inferred from the data:

The input is interpreted as 25 separate paths:

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