.png){kind=icon}
返回
TimeCluster: dimension reduction applied to temporal data for visual analytics
.png){kind=icon}
OA
AI
DOI：10.1007/s00371-019-01673-y.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Time-series data
Visual analytics
Sliding window
Dimension reduction
Time-series graph
2D projection
Repeated patterns
Outliers
Labeling
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.9
论文数:
4.7K
被引数:
6.5K
机构
引用论文
.png){kind=icon}
Trade-Off between Carbon Dioxide Emissions and Logistics Costs Based on Multiobjective Optimization基于多目标优化的二氧化碳排放与物流成本权衡OAAI
OAAI分享收藏High Efficiency Large Area Back Contact Concentrator Solar Cells with a Multilevel Interconnection
PREAI
分享收藏
查看更多内容