Data Visualization of the Time-Varying Multivariate Data - Harsha Bhat (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Data can be tracked down in various configurations, including value, size, weight, and variety data for each thing an organization sells, or time series of day to day perceptions of temperature, precipitation, wind, and deceivability from large number of locales. It is inherently difficult to develop a comprehensive overview and comprehension of them due to their vastness and complexity. By turning data into more understandable representations, information visualization seeks to get around these challenges. In the space of social exploration, natural checking, money and financial matters, wellbeing, and geographic data, a lot of time-varying multivariate data has been created. Understanding complicated and dynamic variable interaction and temporal evolution requires effective time-varying multivariate data processing and visualization. Most of this field’s achievements have been in the space of relationship finding and question driven visualization. Strategies or arrangements have not checked out at the essential issue of causal connections between factors. In this review, we present a creative way to deal with the examination and visualization of time-varying multivariate volumetric and molecule data sets. This approach depends on the data hypothetical idea of move entropy and the assessment of data stream.