Time Series Analysis Exponential Smoothing Methods

Question 1

What is the key idea behind simple exponential smoothing (SES)?

Accepted Answer

Recent observations receive more weight than older ones via a decay parameter α

Answer

All observations are weighted equally

Answer

Only the most recent observation is used

Answer

Weights increase linearly with time

Question 2

In simple exponential smoothing, what does a smoothing parameter α close to 1 indicate?

Accepted Answer

The forecast reacts quickly to recent changes

Answer

The forecast is very stable and slow to update

Answer

Past observations are weighted heavily

Answer

The forecast is identical to the mean

Question 3

What does Holt's linear trend method add compared to simple exponential smoothing?

Accepted Answer

A second smoothing equation to track the trend component

Answer

A third equation for seasonality

Answer

Automatic outlier detection

Answer

A multiplicative error term

Question 4

What is the Holt-Winters method designed to handle?

Accepted Answer

Time series with both trend and seasonality

Answer

Non-seasonal stationary series only

Answer

Series with no trend

Answer

Series with only cyclical components

Question 5

What is the difference between additive and multiplicative Holt-Winters models?

Accepted Answer

Additive seasonal terms are constant in magnitude; multiplicative terms scale with the level

Answer

Additive uses one parameter; multiplicative uses two

Answer

They produce identical forecasts

Answer

Additive is for weekly data; multiplicative is for monthly data

Question 6

What is the ETS framework in time series modeling?

Accepted Answer

Error, Trend, Seasonality — a unified state-space framework for exponential smoothing

Answer

Estimated Time Series method

Answer

Exponential Trend Smoothing only

Answer

Error Testing System