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 compone

Abstract: A computer-implemented method for training a data-compression system includes an encoder neural network and a memory module. The method includes the steps of iteratively removing from a training dataset laid out sequentially along n dimensions and having a given scale (S1), sub-datasets having a given cutoff scale (S?1). At each successive iteration, the scale of the training dataset increases, as does the cutoff scale of the sub-datasets. A data-compression system has an encoder neural network and a memory module, wherein the encoder neural network has been trained using the described training 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 compone