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TimeSeriesMapThread [f,tseries]

gives {{t1,f[t1,x1]},{t2,f[t2,x2]},} for the time series tseries.

TimeSeriesMapThread [f,tseries,{{a1,a2,},{b1,b2,},}]

gives {{t1,f[t1,x1,a1,b1,]},{t2,f[t2,x2,a2,b2,]},} for the time series tseries.

Details
Details and Options Details and Options
Examples  
Basic Examples  
Scope  
Basic Uses  
Data Types  
Applications  
Inflation  
Weather  
Simulation  
GDP  
Moon Phases  
See Also
Related Guides
History
Cite this Page

TimeSeriesMapThread [f,tseries]

gives {{t1,f[t1,x1]},{t2,f[t2,x2]},} for the time series tseries.

TimeSeriesMapThread [f,tseries,{{a1,a2,},{b1,b2,},}]

gives {{t1,f[t1,x1,a1,b1,]},{t2,f[t2,x2,a2,b2,]},} for the time series tseries.

Details

Examples

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Basic Examples  (2)

Apply a function f to the times and values in a time series:

Create a series with increasing variance as a function of time:

Add quadratic trend to the values:

Scope  (7)

Basic Uses  (2)

Set a value at time 3 to 0:

Add time-dependent noise to a vector-valued signal:

Data Types  (5)

Apply a function f to a list of time-value pairs:

Set the values for times greater than 3 in TimeSeries to 1:

Visualize TemporalData for integer times:

Set the middle 50 values of EventSeries to 0:

Modify the values of a time series involving quantities:

Modify the values for times greater than 2:

Applications  (5)

Inflation  (1)

The following are spot oil prices from 1970 to 2010. This data is not corrected for inflation:

The consumer price index (CPI) is often used to correct historical prices for inflation:

Adjusted prices for inflation using the most recent value of the CPI:

Find the adjusted oil price on March 5, 2001:

Weather  (1)

The following are monthly average temperatures for a Missouri city over a three-year period. Separate the seasonal and nonseasonal components of this data:

Fit a model for the seasonal component:

Use the model to obtain the nonseasonal component:

Combining the components yields the original data:

Simulation  (1)

Use TimeSeriesMapThread to simulate a transformed random process:

Simulate WienerProcess :

Apply transformation to the random sample to obtain Brownian bridge:

Compare to the corresponding BrownianBridgeProcess :

GDP  (1)

Study the discrepancy in the estimate of GDP for Switzerland, in dollars:

The currency in which data is given:

Transfer to US dollars and adjust for inflation:

Moon Phases  (1)

Create a lunar calendar in which the background color depends on the fraction of the moon illumination:

Create a new series of labeled images with gradient background:

Visualize the lunar calendar:

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

Text

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

CMS

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

APA

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

BibTeX

@misc{reference.wolfram_2025_timeseriesmapthread, author="Wolfram Research", title="{TimeSeriesMapThread}", year="2014", howpublished="\url{https://reference.wolfram.com/language/ref/TimeSeriesMapThread.html}", note=[Accessed: 05-December-2025]}

BibLaTeX

@online{reference.wolfram_2025_timeseriesmapthread, organization={Wolfram Research}, title={TimeSeriesMapThread}, year={2014}, url={https://reference.wolfram.com/language/ref/TimeSeriesMapThread.html}, note=[Accessed: 05-December-2025]}

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