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**Class: **arima

Forecast ARIMA or ARIMAX process

```
[Y,YMSE]
= forecast(Mdl,numPeriods)
```

[Y,YMSE,V]
= forecast(Mdl,numPeriods)

[Y,YMSE,V] = forecast(Mdl,numPeriods,Name,Value)

`[`

forecasts
responses for a univariate ARIMA model, and generates corresponding
mean square errors, `Y`

,`YMSE`

]
= forecast(`Mdl`

,`numPeriods`

)`YMSE`

.

`[`

additionally
forecasts conditional variances for an ARIMA model with a conditional
variance model.`Y`

,`YMSE`

,`V`

]
= forecast(`Mdl`

,`numPeriods`

)

`[Y,YMSE,V] = forecast(Mdl,numPeriods,`

generates
the forecasts with additional options specified by one or more `Name,Value`

)`Name,Value`

pair
arguments.

[1] Baillie, R., and T. Bollerslev. “Prediction in
Dynamic Models with Time-Dependent Conditional Variances.” *Journal
of Econometrics*. Vol. 52, 1992, pp. 91–113.

[2] Bollerslev, T. “Generalized Autoregressive Conditional
Heteroskedasticity.” *Journal of Econometrics*.
Vol. 31, 1996, pp. 307–327.

[3] Bollerslev, T. “A Conditionally Heteroskedastic
Time Series Model for Speculative Prices and Rates of Return.” *The
Review Economics and Statistics*. Vol. 69, 1987, pp. 542–547.

[4] Box, G. E. P., G. M. Jenkins, and G. C. Reinsel. *Time
Series Analysis: Forecasting and Control* 3rd ed. Englewood
Cliffs, NJ: Prentice Hall, 1994.

[5] Enders, W. *Applied Econometric Time Series*.
Hoboken, NJ: John Wiley & Sons, 1995.

[6] Engle, R. F. “Autoregressive Conditional Heteroskedasticity
with Estimates of the Variance of United Kingdom Inflation.” *Econometrica*.
Vol. 50, 1982, pp. 987–1007.

[7] Hamilton, J. D. *Time Series Analysis*.
Princeton, NJ: Princeton University Press, 1994.

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