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Simulate Multiplicative ARIMA Models

This example shows how to simulate sample paths from a multiplicative seasonal ARIMA model using simulate. The time series is monthly international airline passenger numbers from 1949 to 1960.

Load the Data and Estimate a Model.

Load the data set Data_Airline.

y = log(Data);
T = length(y);

Mdl = arima('Constant',0,'D',1,'Seasonality',12,...
EstMdl = estimate(Mdl,y);
    ARIMA(0,1,1) Model Seasonally Integrated with Seasonal MA(12) (Gaussian Distribution):
                  Value      StandardError    TStatistic      PValue  
                _________    _____________    __________    __________

    Constant            0              0           NaN             NaN
    MA{1}        -0.37716       0.066794       -5.6466      1.6364e-08
    SMA{12}      -0.57238       0.085439       -6.6992      2.0952e-11
    Variance    0.0012634     0.00012395        10.193      2.1406e-24
res = infer(EstMdl,y);

Simulate Airline Passenger Counts.

Use the fitted model to simulate 25 realizations of airline passenger counts over a 60-month (5-year) horizon. Use the observed series and inferred residuals as presample data.

Ysim = simulate(EstMdl,60,'NumPaths',25,'Y0',y,'E0',res);
mn = mean(Ysim,2);

hold on
h = plot(T+1:T+60,mn,'k--','LineWidth',2);
title('Simulated Airline Passenger Counts')
legend(h,'Simulation Mean','Location','NorthWest')
hold off

Figure contains an axes object. The axes object with title Simulated Airline Passenger Counts contains 27 objects of type line. This object represents Simulation Mean.

The simulated forecasts show growth and seasonal periodicity similar to the observed series.

Estimate the Probability of a Future Event.

Use simulations to estimate the probability that log airline passenger counts will meet or exceed the value 7 sometime during the next 5 years. Calculate the Monte Carlo error associated with the estimated probability.

rng default
Ysim = simulate(EstMdl,60,'NumPaths',1000,'Y0',y,'E0',res);

g7 = sum(Ysim >= 7) > 0;
phat = mean(g7)
phat = 0.3910
err = sqrt(phat*(1-phat)/1000)
err = 0.0154

There is approximately a 39% chance that the (log) number of airline passengers will meet or exceed 7 in the next 5 years. The Monte Carlo standard error of the estimate is about 0.02.

Plot the Distribution of Passengers at a Future Time.

Use the simulations to plot the distribution of (log) airline passenger counts 60 months into the future.

title('Distribution of Passenger Counts in 60 months')

Figure contains an axes object. The axes object with title Distribution of Passenger Counts in 60 months contains an object of type histogram.

See Also

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