Patent ID: 7103502

Claim:
A two-stage method for characterizing sparse data of a time series distribution, said method comprising the steps of: creating a virtual window having a two-dimensional area containing a distribution with a maximum number N of data points of said sparse data for a selected time period; subdividing substantially the entirety of said area of said virtual window into a plurality k of cells wherein said plurality k of cells have an identical polygonal shape and each cell defines an identical area; determining a false alarm probability α based on a total number of said plurality of k cells; providing a first stage of characterization said sparse data comprising: determining a number of said plurality of k cells in a particular sample containing at least one of said data points of said time series distribution, said determined number being identified as m; determining an expected proportion Θ of k cells containing at least one of said data points in the event of a random distribution; and calculating a lower random boundary m 1 from the false alarm probability α; calculating an upper random boundary m 2 from the false alarm probability α; providing a first stage of characterization of said sparse data by characterizing said input time series as a random distribution by said first stage of characterization if m is greater than m 2 or if m is less than m 1 ; providing a second stage of characterization of said sparse data comprising: when Θ is less than a pre-selected value, then utilizing a Poisson distribution to determine a first mean of said data points; when Θ is greater than said pre-selected value, then utilizing a binomial distribution to determine a second mean of said data points; computing a probability p from said first mean or said second mean depending on whether Θ is greater than or less than said pre-selected value; comparing p with α to determine whether to characterize said sparse data as noise or signal by said second stage of characterization wherein said characterization is mathematically stated as; if p≧α=>sparse data is characterized as NOISE; if p<α=>sparse data is characterized as SIGNAL comparing said first stage of characterization of said sparse data with said second stage of characterization of said sparse data; and if said first stage characterization of said sparse data indicates a random distribution and said second stage indicates a random distribution, then labeling said sparse data as random.