Algorithm for Autocorrelation Analysis of Secular Trends

Abstract

An algorithm for autocorrelation analysis of time series data was presented and compared with two alternative computational methods. The algorithm was based on continuous variance adjustment following withdrawal of an element from the lagged series. The algorithm not only reduces the number of computations during the repeated passes through the data vector but also avoids a dampening effect toward the end of the series.