DSE Time Series Analysis and Forecasting 2

Question 1

In an ARIMA(p, d, q) model, what does the parameter 'p' represent?

Accepted Answer

The order of the autoregressive component

Answer

In ARIMA(p, d, q), p is the order of the AutoRegressive component, specifying how many lagged values of the series are included as predictors.

Question 2

Which plot is used to determine the order of the autoregressive term (p) when building an ARIMA model?

Accepted Answer

PACF (Partial Autocorrelation Function) plot

Answer

The PACF shows the direct correlation between a series and its lags after removing the effect of shorter intermediate lags, making it the standard tool for selecting the AR order p.

Question 3

What is the key difference between an AR (AutoRegressive) model and an MA (Moving Average) model?

Accepted Answer

AR models use lagged observed values as predictors; MA models use lagged forecast errors as predictors

Answer

An AR model regresses the current value on past observed values of the series, while an MA model uses past forecast errors (residuals) as its predictor inputs.

Question 4

What is the core principle behind exponential smoothing in time series forecasting?

Accepted Answer

Assigning exponentially decreasing weights to past observations so recent data has more influence

Answer

Exponential smoothing applies geometrically declining weights to historical observations, ensuring the most recent values contribute most to the forecast.

Question 5

The Ljung-Box test applied to time series model residuals is used to test for what?

Accepted Answer

Remaining autocorrelation in the residuals

Answer

The Ljung-Box test checks whether the residuals from a fitted model show significant autocorrelation at multiple lags; significant autocorrelation means the model is inadequate.

DSE - Data Science Practice Test

DSE - Data Science Practice Test

DSE Time Series Analysis and Forecasting 2