||Subjective Curve Fitting
Product Life Cycle
Time Independent Technological Comparison
|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.
Prediction Interval Forecasts
("Probabilistic Forecasts diagram") ("Future forecast" really is presnet forecast of future value!)
|Forecast Error = actual -
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.
Mean Absolute Deviation: Being off by 3, plus or minus, is 3 tomes as bad as being off by 1, pluis or minus
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