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TimeSeriesModel[…]

represents the symbolic time series model obtained from TimeSeriesModelFit.

Details and Options
Details and Options Details and Options
Examples  
Basic Examples  
Scope  
Options  
ConfidenceLevel  
"LagMax"  
See Also
Related Guides
History
Cite this Page

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:
  • ConfidenceLevel 95/100confidence level to use »
    "LagMax" 10maximum lag number for autocorrelation and partial correlation values and Ljug-Box test -values and plot range »

Examples

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Basic 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:

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

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: 13-August-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: 13-August-2025]}