1985-1995 Reviews

This study examined 263 data series from a single organization. The series represent the number of telephone lines in use in each of 263 localities. The localities include only those with at least 1000 circuits in period one. The data cover the same time period and consist of 71 monthly observations. Most of the series were decreasing over this period. Two forecasting methods were considered: one was Holt’s exponential smoothing, and the other was a ‘robust trend estimate.’ The robust trend estimate uses the median of the first differences for the series and modifies this to adjust for outliers. Here is the question. Is it better to find a model that works best overall, and to use this (referred to as an ‘aggregate selection’ strategy)? Or should a simulation be done for each series to determine which method is best (’individual selection’)? Or should you simply combine forecasts? Research to date suggests that combining is the best strategy, unless you know which method is best. And determining the best method is, of course, the purpose of simulation. In this study, models were calibrated on periods 1 to 24. Successive updating was then used to simulate over periods 25 to 48. The validation was conducted for periods 49 to 70. Of the three strategies examined, ‘aggregate selection’ is the easiest to
operate. ‘ ‘Individual selection’ is more expensive than ‘aggregate
selection.’ However, The results were that the three ex ante strategies performed almost identically for
short range forecasts (from one to six periods in the future). For longer range forecasts
(12 months ahead), the error from aggregate selection was about six percent higher than
that for the individual and combining strategies. However, ‘ |