TimeCluster: dimension reduction applied to temporal data for visual analytics

There is a need for solutions which assist users to understand long time-series data by observing its changes over time,
finding repeated patterns, detecting outliers, and effectively labeling data instances. Although these tasks are quite distinct
and are usually tackled separately, we present an interactive visual analytics system and approach that can address these
issues in a single system. It enables users to visualize, understand and explore univariate or multivariate long time-series
data in one image using a connected scatter plot. It supports interactive analysis and exploration for pattern discovery and
outlier detection. Different dimensionality reduction techniques are used and compared in our system. Because of its power
of extracting features, deep learning is used for multivariate time-series along with 2D reduction techniques for rapid and
easy interpretation and interaction with large amount of time-series data. We deploy our system with different time-series
datasets and report two real-world case studies that are used to evaluate our system.