applies f to the values in tseries.


  • TimeSeriesMap is used to apply functions to the values in a time series.
  • TimeSeriesMap can be used for regularly and irregularly spaced time series.
  • The input tseries can be a list of values {x1,x2,}, a list of time-value pairs {{t1,x1},{t2,x2},}, a TimeSeries, an EventSeries, or TemporalData.
  • TimeSeriesMap threads pathwise for tseries with multiple paths.


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Basic Examples  (2)

Map a function f over the values in a time series:

Find totals for each value in a multivariate time series:

Scope  (9)

Basic Uses  (3)

Map a function over a time series:

Center a time series:

Use Standardize:

Use TimeSeriesMap to find the norm of each value of a vector-valued time series:

Data Types  (6)

Map a function over a vector:

Set extreme values to Missing for a list of time-value pairs:

Double the values of a TimeSeries:

Add 50 to the values of TemporalData:

Represent the events in an EventSeries by alternating colored disks:

Add a Quantity to a time series with quantities:

Applications  (7)

Visualize current maximum prices of stock over a given investment horizon:

Use TimeSeriesMap to calculate determinants of covariance matrices for an ARProcess:

Replace Missing[] differently for each component of vector-valued TimeSeries using:

Visualize the evolution of rail lines in Japan, starting in 1980:

Check the units in which the length is given:

Convert the length to kilometers:

Study unemployment in France:

Unemployement rates:

Convert rates to fractions:

The unemployment is the corresponding fraction of the labor force:

Each value component corresponds to one gender:

Use TimeSeriesMap to find the total unemployement:

Time series of wind speeds in Champaign, IL, in May 2014:

Specify how to handle missing values:

Use TimeSeriesMap to build the time series for the power output of a 1.5 MW wind turbine:

Visualize the daily moving average of the energy output:

Simulate GeometricBrownianMotionProcess by transforming a WienerProcess where :

Apply the transformation to a random sample to obtain the geometric Brownian motion:

Compare to the corresponding GeometricBrownianMotionProcess:

Both simulations should have the same statistical properties, including for slices:

Properties & Relations  (1)

Some operations can be obtained using listability:

Obtain the same result using listability:

Neat Examples  (2)

Visualize market share of web browsers:

The time series contains the percentage of market share for each month:

List of all browsers:

Extract top seven dominant browsers for each month:

Create a phase plot of sunrise and sunset times through a year:

The values are event times up to a minute:

The data does not account for daylight saving time:

Convert the events to a time in the 24-hour day:

Wolfram Research (2014), TimeSeriesMap, Wolfram Language function, https://reference.wolfram.com/language/ref/TimeSeriesMap.html.


Wolfram Research (2014), TimeSeriesMap, Wolfram Language function, https://reference.wolfram.com/language/ref/TimeSeriesMap.html.


Wolfram Language. 2014. "TimeSeriesMap." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/TimeSeriesMap.html.


Wolfram Language. (2014). TimeSeriesMap. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/TimeSeriesMap.html


@misc{reference.wolfram_2024_timeseriesmap, author="Wolfram Research", title="{TimeSeriesMap}", year="2014", howpublished="\url{https://reference.wolfram.com/language/ref/TimeSeriesMap.html}", note=[Accessed: 24-July-2024 ]}


@online{reference.wolfram_2024_timeseriesmap, organization={Wolfram Research}, title={TimeSeriesMap}, year={2014}, url={https://reference.wolfram.com/language/ref/TimeSeriesMap.html}, note=[Accessed: 24-July-2024 ]}