Compact representation and time series segment retrieval through deep learning

A processing system for retrieving similar multivariate time series segments, comprising:

• one or more processors;
• a memory coupled to the one or more processors;
• a long feature extractor stored in the memory, wherein the long feature extractor is configured to extract long feature vectors from multivariate time series segments;
• a short feature extractor stored in the memory, wherein the short feature extractor is configured to convert the long feature vectors generated by the long feature extractor into shorter length features through a linear mapping;
• a long binary extractor stored in the memory, wherein the long binary extractor is configured to convert the long feature vectors from the long feature extractor into long binary codes having a same length as the long feature vectors;
• a short binary extractor stored in the memory, wherein the short binary extractor is configured to convert short feature vectors from the short feature extractor into short binary codes having the same length as the short feature vectors;
• a neural network trained to preserve similarity measure relations from the short binary codes and the long binary codes with triplet losses based on a maximum between zero and a sum of a margin value and a difference of a first Hamming distance between an output value of a hash function for an anchor class label and an output value of the hash function for a positive class label and a second Hamming distance between the output value of the hash function for the anchor class label and an output value of the hash function for a negative class label and cross-entropy loss based on a product of a one-hot representation of a class label and a logarithm of a predicted probability for a segment from the multivariate time series segments, and a one vector with a class label set length, to utilize label information for capturing a global decision boundary in a feature space from the multivariate time series segments;
• and
• a similarity comparator stored in the memory, wherein the similarity comparator is configured to calculate a pairwise similarity measures between the long binary codes extracted from query and the long binary codes retrieved from a dictionary, and reduces memory complexity and usage by compressing corresponding data for the multivariate time series segments for downstream tasks by identifying a predetermined number of dictionary long codes having the pairwise similarity measures indicating a closest relationship between the long binary codes and dictionary long codes.

A computer program product for retrieving similar multivariate time series segments, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:

• converting long feature vectors and short feature vectors extracted from multivariate time series segments into long binary codes and short binary codes, respectively;
• training a neural network that preserves similarity measure relations from the short binary codes and the long binary codes with triplet losses and cross-entropy loss to utilize label information for capturing a global decision boundary in a feature space from the multivariate time series segments including:
• computing a first Hamming distance between an output value of a hash function for an anchor class label and an output value of the hash function for a positive class label;
• computing a second Hamming distance between the output value of the hash function for the anchor class label and an output value of the hash function for a negative class label;
• computing the triplet losses based on a maximum between zero and a sum of a margin value and a difference of the first Hamming distance and the second Hamming distance;
• computing the cross-entropy loss based on a product of a one-hot representation of a class label and a logarithm of a predicted probability for a segment from the multivariate time series segments, and a one vector with a class label set length;
• obtaining, with the neural network, a subset of long binary codes from a binary dictionary storing dictionary long codes based on the short binary codes;
• calculating similarity measures for each pair of the long feature vectors with each dictionary long codes;
• identifying, with the neural network, a predetermined number of dictionary long codes having the similarity measures indicating a closest relationship between the long binary codes and the dictionary long codes;
• and
• reducing memory complexity and usage by compressing corresponding data for the multivariate time series segments for downstream tasks by retrieving a predetermined number of time series segments associated with the predetermined number of dictionary long codes.