Method and system for time series representation learning via dynamic time warping

Abstract

Techniques that facilitate time series analysis using machine learning are provided. In one example, a system includes a matrix generation component, a matrix factorization component and a machine learning component. The matrix generation component converts at least a first stream of time series data and a second stream of time series data (e.g., raw time series data) into a data matrix (e.g., a partially-observed similarity matrix) that comprises void data and numerical data associated with the first stream of time series data and the second stream of time series data. The matrix factorization component factorizes the data matrix into a first factorization data matrix and a second factorization data matrix. The machine learning component processes a machine learning model based on first matrix data associated with the first factorization data matrix and second matrix data associated with the second factorization data matrix.