Question

Which is not a characteristic of exponential smoothing?

1)Directly accounts for forecast error
2) Provides an easily altered weighting scheme
3) Smooths random variations in the data
4) Weights each historical value equally
5) Smooths real variations in the data

Answer 1

Exponential smoothing does not weight each historical value equally, which is the option that does not describe this forecasting method. Instead, it applies heavier weights to more recent data points and lowers the impact of older data.

Step-by-step explanation:

The characteristic that is not a feature of exponential smoothing is that it weights each historical value equally. Exponential smoothing is a time series forecasting method for univariate data that can be used to smooth out the series. This forecast approach weights the most recent data more heavily and applies diminishing weights to older data points. The characteristics of exponential smoothing include:

• It directly accounts for forecast error by adjusting future forecasts based on past forecast errors.
• Provides an easily altered weighting scheme, where recent observations can be given more weight.
• Smooths random variations in the data, which makes this method suitable for time series data that has significant noise.
• Does not weight each historical value equally, which is contrary to simple moving averages.
• By focusing more on recent data, it smooths real variations moderately, not as much as it would if all data points were weighted equally.