Data Discovery Analysis on Complex Time Series Data

Abstract

Complex time series are a ubiquitous form of data in the modern world. They have wide application across many different fields of scientific inquiry and business endeavor. Time series are used to understand and forecast weather patterns, voting patterns, computer network traffic, population health outcomes, demographic changes, the results of scientific experiments, and the performance of stocks and mutual funds. But time series can be difficult to analyze by conventional methods when the data is multivariate, incomplete, or in different formats. To address these issues, an investigation of several multivariate time series datasets was performed using the methods of automatic data discovery and derivative-based analysis. Interactive maps were constructed which displayed the results of the study. Conclusions were drawn and discussed, and an explanation was given of how this method can be applied to other multivariate time series datasets and real-world problems.