Exponential smoothing uses a smoothing constant to weight the most recent observations more heavily. Which statement is true?

Exponential smoothing uses a smoothing constant to weight the most recent observations more heavily. Which statement is true?

• A. It assigns equal weight to all past observations
• B. It weights only the oldest data
• C. It ignores recent data
• D. It weights the most recent observations more heavily

Answer: (D)

Exponential smoothing relies on a smoothing constant to control how much weight the most recent data points receive in shaping the forecast. The forecast update combines the latest observation with the previous forecast, using a smoothing constant between 0 and 1. This setup gives more influence to the newest observation, while past data fade in influence as they recede in time. That’s why the most recent observations are weighted more heavily, and why forecasts respond to recent changes when the smoothing constant is larger (and smooth more when it’s smaller). This isn’t a method with equal weights for all past observations—that would be a simple moving average. It doesn’t weight only the oldest data, and it certainly doesn’t ignore recent data. The essence is that recent observations have greater influence, with older data diminishing exponentially over time.

Exponential smoothing relies on a smoothing constant to control how much weight the most recent data points receive in shaping the forecast. The forecast update combines the latest observation with the previous forecast, using a smoothing constant between 0 and 1. This setup gives more influence to the newest observation, while past data fade in influence as they recede in time. That’s why the most recent observations are weighted more heavily, and why forecasts respond to recent changes when the smoothing constant is larger (and smooth more when it’s smaller).

This isn’t a method with equal weights for all past observations—that would be a simple moving average. It doesn’t weight only the oldest data, and it certainly doesn’t ignore recent data. The essence is that recent observations have greater influence, with older data diminishing exponentially over time.