The bias of forecasts is a measure of the tendency of a model to systematically under- or over-predict71. The bias of a set of predictions I^jt at time t at location j is defined as

where the mean is taken across the N draws. H(x) is the Heaviside step function defined as

The above formulation can better be understood by considering the following extreme scenarios. If every projected value I^jt is greater than the observed value Ijt, then the Heaviside function is 1 for all i = 1, 2, …N, and meanHI^jtIjt is 1. The bias for a model that always over-predicts is therefore 1. On the other hand, if the model systematically under-predicts, then meanHI^jtIjt is 0 and the bias is -1. For a model for which all predictions match the observed values exactly, the bias is 0.

Note: The content above has been extracted from a research article, so it may not display correctly.

Please log in to submit your questions online.
Your question will be posted on the Bio-101 website. We will send your questions to the authors of this protocol and Bio-protocol community members who are experienced with this method. you will be informed using the email address associated with your Bio-protocol account.

We use cookies on this site to enhance your user experience. By using our website, you are agreeing to allow the storage of cookies on your computer.