Time-Series Methods
Rather than using independent variables for the forecast as regression models do, time-series methods use historical information regarding only the dependent variable. These methods are based on the assumption that the dependent variable’s past pattern will continue in the future. Time-series analysis identifies the underlying patterns of demand that combine to produce an observed historical pattern of the dependent variable and then develops a model to replicate it. In this section, we focus on five statistical time-series methods that address the horizontal, trend, and seasonal patterns of demand: simple moving averages, weighted moving averages, exponential smoothing, trend projection with regression, and multiplicative ...
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