BACHELOR OF BUSINESS ADMINISTRATION

Detailed explanation-1: -Exponential Smoothing forecasts future values by taking the weighted average of previous values. It calculates the weighted average by using a smoothing factor (). The exponential smoothing method is adaptive for recent changes in the data points.

Detailed explanation-2: -A forecasting technique referred to as moving averages uses the average or mean of the most recent n periods to forecast the next value for time series data. With a three-period moving average, the most recent three periods of data are used in the forecast computation.

Detailed explanation-3: -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-4: -This simple form of exponential smoothing is also known as an exponentially weighted moving average (EWMA). Technically it can also be classified as an autoregressive integrated moving average (ARIMA) (0, 1, 1) model with no constant term.

Detailed explanation-5: -Exponential smoothing refers to the use of an exponentially weighted moving average (EWMA) to “smooth” a time series.