scholarly journals Visual analytics techniques for large multi-attribute time series data

2008 ◽  
Author(s):  
Ming C. Hao ◽  
Umeshwar Dayal ◽  
Daniel A. Keim
Keyword(s):  
Time Series ◽  
Visual Analytics ◽  
Time Series Data ◽  
Series Data

Visual analytics of phosphorylation time-series data on insulin response

2013 ◽  
Author(s):  
David K. G. Ma ◽  
Christian Stolte ◽  
Sandeep Kaur ◽  
Michael Bain ◽  
Seán I. O'Donoghue
Keyword(s):  
Time Series ◽  
Visual Analytics ◽  
Time Series Data ◽  
Insulin Response ◽  
Series Data

scholarly journals A Visual Analytics Platform and Advanced Visualization Tools for Interpreting and Analyzing Wind Energy Time-Series Data

2018 ◽  
Vol 51 (28) ◽  
pp. 480-485 ◽  
Author(s):  
George Chin ◽  
Yousu Chen ◽  
Erin Fitzhenry ◽  
Blaine McGary ◽  
Meg Pirrung ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Energy ◽  
Visual Analytics ◽  
Time Series Data ◽  
Series Data ◽  
Visualization Tools ◽  
Advanced Visualization

Interactive Visualization Adopting Dimensionality Reduction Techniques for Pattern Recognition in Large Temporal Datasets

2021 ◽  
Author(s):  
◽  
Mohammed Ali
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Dimensionality Reduction ◽  
Visual Analytics ◽  
Large Time ◽  
Time Series Data ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques

In this thesis, we focus on time-series data, which is commonly used by domain experts in different domains to explore and understand phenomena or behaviors under consideration, as-sisting them in making decisions, predicting the future or solving problems. Utilizing sensor devices is one of the common ways of collecting time-series data. These devices collect large volumes of raw data, including multi-dimensional time-series data, and each value is associated with the time-stamp corresponding to when it was recorded. However, finding interesting pat-terns or behaviors in a large amount of data is not simple due to the nature of the data and other challenges related to its size and scalability, high dimensionality, complexity, representation, and unique structure.Researchers tend to use time-series chart visualization, which is usually unsuitable because of the small screen resolution which cannot accommodate the large size of the data. Hence, occlusion and overplotting issues occur, limiting or complicating the exploration and analysis tasks. Another challenge concerns the labeling of patterns in large time-series data, which is time-consuming and requires a great deal of expert knowledge.These issues are addressed in this thesis to improve the exploration, analysis and presen-tation of time-series data and enable users to gain insights into large and multi-dimensional time-series datasets using a combination of dimensionality reduction techniques and interac-tive visual methods. The provided solutions will help researchers from various domains who deal with large and multi-dimensional time-series data to efficiently explore and analyze such data with little effort and in record time.Initially, we explore the area of integration between machine learning algorithms and inter-active visualization techniques for exploring and understanding time-series data, specifically looking at clustering and classification for time-series data in visual analytics. The survey is considered to be a valuable guide for both new researchers and experts in the emerging field of integrating machine learning algorithms into visual analytics.Next, we present a novel approach that aims to explore, analyze, and present large temporal datasets through one image. The proposed approach uses a sliding window and dimensionality reduction techniques to depict a large time-series data as points into a 2D scatter plot. The approach provides novel solutions to many pattern discovery issues and can deal with both univariate and multivariate time-series data.Following this, our proposed approach is combined with both visualization and interaction techniques into one system called TimeCluster, which is a visual analytics tool allowing users to visualize, explore and interact with large time-series data. The system addresses different issues such as anomaly detection, the discovery of frequent patterns, and the labeling of in-teresting patterns in large time-series data all in a single system. We deploy our system with different time-series datasets and report real-world case studies of its utility.Later, the linkage between the 1D view (time-series chart) to the 2D view of the 2D embed-ding of time-series data, and parallel interactions such as selection and labeling, are employed to explore and examine the effectiveness of recent developments in machine learning and di-mension reduction in the context of time-series data exploration. We design a user study to evaluate and validate the effectiveness of the linkage between both a 1D and 2D visualization, and how their fitness in the context of projecting time-series data is, where different dimen-sionality reduction techniques are examined, evaluated and compared within our experimental setting.Lastly, we conclude our findings and outline possible areas for future work.

scholarly journals Visual Analytics for Climate Change Detection in Meteorological Time-Series

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 276-289
Author(s):  
Milena Vuckovic ◽  
Johanna Schmidt
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Change Detection ◽  
Visual Analytics ◽  
Structural Changes ◽  
Time Series Data ◽  
Meteorological Data ◽  
Series Data ◽  
Climate Change Signal ◽  
Climate Change Detection

The importance of high-resolution meteorological time-series data for detection of transformative changes in the climate system is unparalleled. These data sequences allow for a comprehensive study of natural and forced evolution of warming and cooling tendencies, recognition of distinct structural changes, and periodic behaviors, among other things. Such inquiries call for applications of cutting-edge analytical tools with powerful computational capabilities. In this regard, we documented the application potential of visual analytics (VA) for climate change detection in meteorological time-series data. We focused our study on long- and short-term past-to-current meteorological data of three Central European cities (i.e., Vienna, Munich, and Zürich), delivered in different temporal intervals (i.e., monthly, hourly). Our aim was not only to identify the related transformative changes, but also to assert the degree of climate change signal that can be derived given the varying granularity of the underlying data. As such, coarse data granularity mostly offered insights on general trends and distributions, whereby a finer granularity provided insights on the frequency of occurrence, respective duration, and positioning of certain events in time. However, by harnessing the power of VA, one could easily overcome these limitations and go beyond the basic observations.

scholarly journals Clustering and Classification for Time Series Data in Visual Analytics: A Survey

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 181314-181338 ◽  
Author(s):  
Mohammed Ali ◽  
Ali Alqahtani ◽  
Mark W. Jones ◽  
Xianghua Xie
Keyword(s):  
Time Series ◽  
Visual Analytics ◽  
Time Series Data ◽  
Series Data ◽  
Clustering And Classification
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