ENTREPRENEURSHIP

Detailed explanation-1: -Moving Average and Exponential Smoothing are two important techniques used for time series forecasting. Moving Average is applied to data to filter random noise from it, while Exponential Smoothing applies exponential window function to data.

Detailed explanation-2: -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-3: -A moving average smoothes a series by consolidating the monthly data points into longer units of time-namely an average of several months’ data. There is a downside to using a moving average to smooth a data series, however. Because the calculation relies on historical data, some of the variable’s timeliness is lost.

Detailed explanation-4: -The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, the main applications being technical analysis and volatility modeling.