On a novel approach to forecast sparse rare events: applications to Parkfield earthquake prediction

Rare events are plentiful in nature and most of them have devastating consequences on human lives and property. Modeling such events is intrinsically challenging due to their very characteristic of rarity, and at times, reliable forecasts are immensely difficult to obtain due to the dearth of available data. Such situations are commonplace and typically happen when we are not equipped with a sufficiently rich historical record faithfully following the event of interest. The purpose of this endeavor is to promote the use of a certain kind of smoothing statistic termed empirical recurrence rate which can generate pseudodata over barren observation periods and to realize that such a method can effectively enlarge the size of the data set and thereby generate better prediction power. It’s simple method of construction appeals to intuition and hence, it should be profitably applied to analyze events originating from such diverse disciplines as meteorology, medical science, oceanography, volcanology, seismology, etc. We illustrate the applicability of our method with the aid of historical records of strong earthquakes at Parkfield, California, and describe how it triumphs over more established methods from a forecasting viewpoint. Copyright Springer Science+Business Media Dordrecht 2015