Higher values of alpha in exponential smoothing means the last data point is more important than previous forecasts Select one: True False
The correct answer and explanation is:
The correct answer is: True
Explanation:
In exponential smoothing, alpha (α) is the smoothing constant or smoothing factor that determines the weight given to the most recent actual data point compared to previous forecasts. The value of alpha ranges between 0 and 1. A higher alpha means that the model places more emphasis on the most recent observation, making the forecast more sensitive to recent changes in the data.
When alpha is close to 1, the exponential smoothing model responds quickly to changes because it gives a greater weight to the latest data point and less weight to the past forecasts. This makes the forecast adapt rapidly to recent trends or shifts in the data, but it can also make the forecast more volatile or reactive to random fluctuations or noise.
On the other hand, a lower alpha value means the model puts more weight on the previous forecast and less on the most recent data. This results in smoother forecasts that react more slowly to changes in the data. It is useful when the data is noisy or when you want to avoid overreacting to random variations.
The exponential smoothing formula is:
Forecast for next period = (α × Actual current value) + (1 − α) × Previous forecast
Here, the α multiplies the actual current value, and (1 − α) multiplies the previous forecast. Thus, a higher α increases the influence of the current observation.
In summary, the statement is true because a higher alpha value makes the last data point more important in the forecasting process than the previous forecast values, allowing the model to quickly incorporate recent data changes into future predictions.