TimeSeriesModel […]
represents the symbolic time series model obtained from TimeSeriesModelFit .
TimeSeriesModel
TimeSeriesModel […]
represents the symbolic time series model obtained from TimeSeriesModelFit .
Details and Options
- Properties of a time series model are obtained from TimeSeriesModel […]["property"].
- The value of the model at time t can be obtained by giving model[t]. If t is in the range of the input data, then the data at time t is returned; otherwise, a forecasted value is given.
- Forecast prediction limits at a time t can be obtained using model["PredictionLimits"][t].
- Normal gives the underlying time series process for the time series model.
- TimeSeriesModel […][prop,ann] gives the annotation ann associated with the property prop.
- Possible time series model properties are listed on the page for TimeSeriesModelFit .
- TimeSeriesModel takes the following options:
-
"LagMax" 10 maximum lag number for autocorrelation and partial correlation values and Ljug-Box test -values and plot range »
Examples
open all close allBasic Examples (1)
Create a TimeSeriesModel from some data:
Extract a property from the model:
Evaluate the time series model at time 100:
Use Normal to obtain the underlying time series process:
Obtain a list of available properties:
Scope (7)
Extract a property from a TimeSeriesModel :
The BIC value for the model:
Obtain a list of properties:
Evaluate the time series model at a point:
Map over several points:
Evaluate the time series model at a date:
Obtain the underlying time series process:
Annotate a property:
Use the original data to simulate future observations with TimeSeriesModel :
Fit a model using TimeSeriesModelFit :
Use the best fit process to simulate 10 future observations:
No information about time stamps or initial values is passed to RandomFunction :
Simulate using TimeSeriesModel to use information given by the original data:
Options (3)
ConfidenceLevel (1)
Set the confidence level to use for prediction limits:
Use a 90% confidence level:
The confidence level can be set before fitting:
The default confidence level is 95%:
"LagMax" (2)
Obtain values used in residual whiteness diagnostic plots:
The first 5 autocorrelation values and 95% confidence region used in the "ACFPlot":
The first 5 partial autocorrelation values and 95% confidence region used in the "PACFPlot":
The Ljung–Box -values for lags 1 to 12 and the critical value used in the plot:
Test model residuals for autocorrelation:
Plot the results of Ljung-Box test:
Change the maximum lag in Ljung-Box test:
Related Guides
History
Text
Wolfram Research (2014), TimeSeriesModel, Wolfram Language function, https://reference.wolfram.com/language/ref/TimeSeriesModel.html.
CMS
Wolfram Language. 2014. "TimeSeriesModel." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/TimeSeriesModel.html.
APA
Wolfram Language. (2014). TimeSeriesModel. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/TimeSeriesModel.html
BibTeX
@misc{reference.wolfram_2025_timeseriesmodel, author="Wolfram Research", title="{TimeSeriesModel}", year="2014", howpublished="\url{https://reference.wolfram.com/language/ref/TimeSeriesModel.html}", note=[Accessed: 05-December-2025]}
BibLaTeX
@online{reference.wolfram_2025_timeseriesmodel, organization={Wolfram Research}, title={TimeSeriesModel}, year={2014}, url={https://reference.wolfram.com/language/ref/TimeSeriesModel.html}, note=[Accessed: 05-December-2025]}