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.
Visual analytics for time series data has received a considerable amount of attention. Different approaches have been developed to understand the characteristics of the data and obtain meaningful statistics in order to explore the underlying processes, identify and estimate trends, make decisions and predict the future. The machine learning and visualization areas share a focus on extracting information from data. In this paper, we consider not only automatic methods but also interactive exploration. The ability to embed efficient machine learning techniques (clustering and classification) in interactive visualization systems is highly desirable in order to gain the most from both humans and computers. We present a literature review of some of the most important publications in the field and classify over 60 published papers from six different perspectives. This review intends to clarify the major concepts with which clustering or classification algorithms are used in visual analytics for time series data and provide a valuable guide for both new researchers and experts in the emerging field of integrating machine learning techniques into visual analytics.
We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.