Asynchronous intercorrelated time series datasets alignment method

Abstract

A computer-implemented method for aligning intercorrelated asynchronous time series datasets includes the steps of: (a) retrieving a first time series dataset (x) and a second time series dataset (y), the first and second time series dataset being intercorrelated, (b) segmenting each of the first and second time series dataset (x, y) into a plurality of consecutive smaller segments (xi, yi), all segments of the first and second time series dataset (x, y) having the same length, (c) determining pairs of corresponding segments by associating successive segments of the first time series dataset (x) with corresponding segments of the second time series dataset (y), (d) optimizing, for each pair of corresponding segments, a correlation function to obtain an approximation of a first times series transformation function (f1) and of a second time series transformation function (f2), (e) using the first and second time series transformation functions (f1,f2) to determine a vector of segment shifts (s) whose components contain approximations of the shifts between the first and the second segment in a pair of corresponding segments (xi, yi), (f) applying a multi-model fitting algorithm to the segment shift vector (s), said multi-model algorithm outputting a shift function (fopt) for aligning segments of each pair of corresponding segments (xi, yi), and (g) aligning the first time series dataset (x) with the second series dataset by applying said shift function (fopt) to all pairs of corresponding segments (xi, yi), wherein the first time series transformation function (f1) is parametrized by weights (w1) of a first neural network (N1) and outputs a first highly correlated time series dataset, and wherein the second time series transformation function (f2) is parametrized by weights (w2) of a second neural network (N2) and outputs a second highly correlated time series dataset.

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