BACHELOR OF BUSINESS ADMINISTRATION

Detailed explanation-1: -Whereas in Moving Averages the past observations are weighted equally, Exponential Smoothing assigns exponentially decreasing weights as the observation get older. In other words, recent observations are given relatively more weight in forecasting than the older observations.

Detailed explanation-2: -Exponential smoothing is a method for forecasting univariate time series data. It is based on the principle that a prediction is a weighted linear sum of past observations or lags. The Exponential Smoothing time series method works by assigning exponentially decreasing weights for past observations.

Detailed explanation-3: -For a given average age (i.e., amount of lag), the simple exponential smoothing (SES) forecast is somewhat superior to the simple moving average (SMA) forecast because it places relatively more weight on the most recent observation–i.e., it is slightly more “responsive” to changes occuring in the recent past.

Detailed explanation-4: -Exponential Moving Average (EMA) and Simple Moving Average (SMA) are similar in that they each measure trends. The two averages are also similar because they are interpreted in the same manner and are both commonly used by technical traders to smooth out price fluctuations.