Final answer:
Exponential smoothing weights recent observations more heavily by using a smoothing constant called alpha (α). Alpha determines the weight of the latest observation in the forecast with a higher value indicating more emphasis on the latest data. The method updates forecasts to reflect recent trends more quickly than older ones.
Step-by-step explanation:
In exponential smoothing, more recent observations are weighted more heavily than older observations through the use of a smoothing constant, typically denoted as α (alpha). This constant can range from 0 to 1, where higher values of α place more emphasis on the most recent observations.
The basic formula for single exponential smoothing is:
St = αYt + (1-α)St-1
where:
St is the smoothed statistic for the current period,
Yt is the actual value at time t, and
St-1 is the smoothed statistic for the previous period.
This weighted averaging effect ensures that as new data comes in, the forecasts are updated to reflect recent trends more quickly than older trends, which is particularly useful in time-series analysis where data patterns can change over time.