Time Series Analysis Seasonal Decomposition 2

Question 1

What is the main advantage of STL over classical additive decomposition?

Accepted Answer

STL handles any type of seasonality and is robust to outliers

Answer

STL's Loess-based smoothing adapts to changing seasonal patterns and is less distorted by extreme values than classical methods.

Question 2

In classical decomposition, what is the 'centered moving average' used to estimate?

Accepted Answer

The trend-cycle component

Answer

A centered moving average over a full seasonal period smooths out seasonality, leaving an estimate of the underlying trend-cycle.

Question 3

What does a 'remainder' (residual) component after decomposition ideally look like?

Accepted Answer

White noise with no systematic patterns

Answer

If the decomposition is successful, the remainder should be unpredictable white noise with no remaining autocorrelation or patterns.

Question 4

What is 'seasonal adjustment'?

Accepted Answer

Removing the estimated seasonal component from the original series

Answer

Seasonal adjustment subtracts (or divides out) the seasonal component to reveal the underlying trend and irregular movements.

Question 5

Which Python library provides the `seasonal_decompose` function?

Accepted Answer

statsmodels

Answer

statsmodels.tsa.seasonal contains `seasonal_decompose`, which performs classical additive or multiplicative decomposition.

Time Series Analysis Practice Test