Final answer:
Exponential smoothing is a time series forecasting technique that involves assigning decreasing weights to past observations. The exponential part in exponential smoothing refers to the decreasing weights assigned to past observations.
Step-by-step explanation:
Exponential smoothing is a technique used in time series forecasting to calculate future values based on past observations. It involves smoothing out the fluctuations in data by assigning weights to past observations, with the weights decreasing exponentially as the observations get older.
The exponential part in exponential smoothing comes from the fact that the weights given to past observations decrease exponentially with time. This means that more recent observations have a higher weight or influence on the forecasted value, while older observations have a lesser impact.
For example, in simple exponential smoothing, the forecasted value for a future period is calculated as a weighted average of the previous period's actual value and the previous forecasted value, with the weights diminishing exponentially as we move further into the past.