Time Series Analysis DATA-SCIENCE-FOUNDATIONS Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   Which smoothing method gives more weight to recent observations and less weight to older observations?

   • ASimple moving average
   • BSeasonal decomposition
   • CDifferencing
   • DExponential smoothing
   Reveal Solution Discussion Select an answer first
2. 17foundation · easy

   What does the Root Mean Squared Error (RMSE) measure in forecast evaluation?

   • AThe percentage of forecasts that were correct.
   • BThe average absolute difference between forecasted and actual values.
   • CThe median absolute error of the forecasts.
   • DThe square root of the average of squared differences between forecasted and actual values.
   Reveal Solution Discussion Select an answer first
4. 18foundation · easy

   In an autocorrelation function (ACF) plot, what does a slow, gradual decay of autocorrelations typically indicate?

   • AThe series is stationary.
   • BThe series has a trend and is non-stationary.
   • CThe series has strong seasonality.
   • DThe series is white noise.
   Reveal Solution Discussion Select an answer first
5. 19application · medium

   A data scientist is analyzing a stationary time series and observes that the autocorrelation function (ACF) has a sharp cutoff after one lag, while the partial autocorrelation function (PACF) decays gradually. Based on these patterns, which ARIMA model is most appropriate?

   • AARIMA(1,0,1)
   • BARIMA(0,0,1)
   • CARIMA(1,0,0)
   • DARIMA(0,1,0)
   Reveal Solution Discussion Select an answer first
6. 20foundation · easy

   Which forecast accuracy metric is expressed as a percentage and is scale-independent, making it useful for comparing forecasts across different data series?

   • ARoot Mean Squared Error (RMSE)
   • BMean Absolute Percentage Error (MAPE)
   • CMean Squared Error (MSE)
   • DMean Absolute Error (MAE)
   Reveal Solution Discussion Select an answer first