Uses historical info regarding only the dependent variable.
Naive forecast - demand for the next period equals demand for the current period; account for trend; best used when demand is horizontal, trend, seasonal pattern
Simple moving average - average of a demand time series by averaging the demand for the n most recent periods; forecast error (demand-forecast);x-week moving average
Weighted moving average - time series method where each historical demand in the average can have its own weight, the sum of weights equals 1; most recent is given the highest value (i.e. 0.5) last the smallest (i.e. 0.2); multiply the values and add all 3;
Exponential smoothing - a weighted moving average method, that calculates the average of a time series by giving recent demands more weight than earlier demands;Example, p. 493;
Including a trend:
Trend adjusted exponential smoothing p.494;
Multiplicative seasonal method p.496
Additive seasonal method
Forecast errors:
bias - always too high or too low
random
Mean squared error, standard deviation, mean absolute deviation - measurement of the dispersion of forecast errors.
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