The document discusses different approaches to time series forecasting and econometric modeling. It recommends that for short-term forecasts covering the next week, month, or year, simple time series models are best. However, for long-term forecasts, causal models are preferable as demand patterns can change over time. The document also notes that a good time series forecast includes the level, trend, seasonal component, business cycle, a predicted value at a specific time point, and an error range.

1. Forecasting Time Series and Econometric Modelling By Dr David Wilson

2. URL’s http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4.htm http://www.autobox.com/xindex.html http://www.decisioneering.com/time-series-forecasting.html

3. More url’s http://www.fenews.com/fen25/aitchison.html

4. The Basics Time Series or Causal modelling? Which should we choose? In the short run , for next week, month or year it is hard to beat simple time series In the long run, it is wiser to use causal models, since patterns change over time that cause changes in demand

5. Time Series Models Simple is good. Begin by using Excel Data Analysis – and the trend projections

6. The components of a forecast The level (average) The trend (a simple projection) The seasonal component The business cycle Error

7. A good forecast needs A value A time for the value to occur – prediction is a number at a time.. 3240 pallets in December 2006. An error range -- + or – some error using maybe a standard deviation some measure of variation