This simulation study of time series data generated using an ARIMA(1,1,1) model. The analysis begins with data generation, followed by visualization and identification of the underlying structure using ACF and PACF plots. Stationarity is evaluated using the Augmented Dickey-Fuller (ADF) test, and differencing is applied to achieve stationarity when necessary. Several candidate ARIMA models are then proposed based on the observed patterns and diagnostic tools. Model selection is performed using the Akaike Information Criterion (AIC) to determine the best-fitting model. The results show that the selected model is consistent with the data-generating process, demonstrating that the model selection procedure is able to capture the dependence structure of the simulated data effectively.

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final paper

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