Chapter 1: Introduction

1.1 Forecasting and Data

What is a Forecast?
Cross-Sectional Data
Time Series Data
Trend,   Cycle,    Seasonal Variation,    Irregular Variation

1.2 Forecasting Methods
 Qualitative Forecasting Methods  ("Judgmental forecasting") Subjective Curve Fitting                             Product Life Cycle Delphi Method Time Independent Technological  Comparison Quantitative Forecasting Methods Univariate Forecasting Models: Everything is a function ot time and/or past y Causal Forecasting Models: The observedvalue of x "causes" us to believe that y will be f(x)                      For example, seeing smoke now (x) causes us to think we will soon see fire (y).              "Leading Indicator Models" is a less misleading name for the same thing.

1.3 Errors in Forecasting                    (Statistics is the Science of Error)
 Types of Forecasts Point Forecasts Prediction Interval Forecasts                    ("Probabilistic Forecasts diagram")  ("Future forecast" really is presnet forecast of future value!)

 Measuring Forecast Errors Forecast Error = actual - forecasted.                            a Positive error means the forecast was Too Low!   (silly, isn't it?) Bias:  It's OK to be too high sometimes and too low other times as long as it averages out. Absolute Deviation Mean Absolute Deviation: Being off by 3, plus or minus, is 3 tomes as bad as being off by 1, pluis or minus Squared Error Mean Squared Error: Being off by 3, plus or minus, is 9 tomes as bad as being off by 1, pluis or minus Absolute Percentage Error Mean Absolute Percentage Error

1.4 Choosing a Forecasting Technique
1. The Time Frame
2. The Pattern of the Data
Based on business knowledge, not statistical fishing!
3. The Cost of Forecasting
Developing the Model
Operating the Model
For quantitative methods, nearly all the cost is obtaining valid data.
For qualitative methods, it's paying experts' salaries or consulting fees.
4. The Accuracy Desired
Based on the cost of error and the accuracy of available approaches
5. The Availability of the Data
What's possible to obtain? How much will it cost (see above)?
How reliable is it?  What will it cost to make it more reliable?
6. The Ease of Operation and Understanding
Data Collection and Data Entry once again
Why should I bet my career on your forecast?