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.
TimeSeriesMapThread
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
- TimeSeriesMapThread 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 .
- TimeSeriesMapThread threads over the paths in tseries.
Examples
open all close allBasic 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:
Related Guides
History
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]}