CHAPTER 4MOVING AVERAGES AND
SMOOTHING METHODS(Page 107) They are based solely on the most recent information available.
Is sometimes called the “no change” forecast.
Suitable for very small data sets.
The simplest model is:
NAÏVE MODELS Pattern of data: ST, stationary; T, trended; S, seasonal; C, cyclical.
Time horizon: S, short term (less than three months); I, intermediate; L, long term
Type of model: TS, time series; C, causal. Seasonal: s, length of seasonality. of Variable: V, number variables. Example 4.1 (Page 108)Table 4-1 Sales of Saws for Acme Tool Company, 2000 – 2006.
Initialization (Fitting) Part: 2000 – 2005.
Test Part: 2006. The technique can be adjusted to take trend into consideration:
The rate of change might be more appropriate than the absolute amount of change: An appropriate forecast equation for quarterly data: For monthly data: The analyst can combine seasonal and trend estimates using: Example 4.1 (cont.)Using Equations (4.3, 4.4, and 4.5)
(Pages 110 – 111) Forecasting Methods Based on Averaging (Page 111) ...