WK1 Model: Prediction Intervals for Your Forecasts
10 Pages Posted: 6 Nov 2011 Last revised: 16 Jun 2014
Date Written: November 6, 2011
This paper sets forth a synergy of existing statistical theories to obtain a clear-cut model for calculating forecasts with prediction intervals, named the “WK1 model.”
Many predictive models calculate a linear or non-linear trend from the historical data and generate a single, discrete forecast value, being a single dot on this defined trend line (i.e. point forecast).
Our “WK1 model” increases the power of such a single discrete point forecast by adding its probable accuracy with top and bottom limits. The decision-maker obtains thus different ranges of values, each within several pre-defined prediction intervals to assess for that specific outcome probability.
The first step is obviously to establish the degree of the predicting power between the two variables that will be used, based on the historical data and their statistical fundamentals (covariance and correlation).
Once the predicting power of one variable for another one is proven, the second step of the “WK1 model” will calculate the trend line in the usual way. Finally, the results of the first two steps are combined with the calculation of the different prediction intervals (e.g. 60% probability, 75%, 90%, 95%, 99%, 99.5%) to provide the decision-maker a forecast supplemented with its prediction intervals (outcome probability), instead of a single point forecast. These ranges are based on the trend line value, but supplemented with calculated probability margins above and below. By doing so, the “WK1 model” thus includes accuracy and reliability to the point values from the trend line.
Keywords: predictive analytics, prediction intervals, correlation, covariance, trends, future values, estimates, WK1 model, Martin van Wunnik, Consolite, ARSIMA, ARSIMA Projects
JEL Classification: C53, C13, C49, D89, G10
Suggested Citation: Suggested Citation