In simple exponential smoothing technique, the more recent data weighted more heavilyGroup of answer choicesTrueFalse

Understanding Simple Exponential Smoothing

1. Definition of Simple Exponential Smoothing: Simple exponential smoothing is a forecasting technique used for time series data where recent observations are given more weight than older observations. This method is particularly useful when there is no trend or seasonal pattern in the data.

2. Weighting of Data: In this technique, the most recent observation is given the highest weight, and the weight decreases exponentially for older observations. The parameter $\alpha$ (alpha) is a smoothing constant that determines the weight given to the most recent observation compared to the previous forecast.

3. Conceptual Illustration: The formula for simple exponential smoothing is expressed as: $F_t = \alpha Y_t + (1 - \alpha) F_{t-1}$ where:

   • $F_t$ = the forecast for the current period
   • $Y_t$ = the actual value observed in the current period
   • $F_{t-1}$ = the forecast for the previous period
   • $\alpha$ = smoothing constant, where $0 < \alpha < 1$

Conclusion

Given that recent data points indeed receive a greater weight compared to older data points in this technique, the statement is True.