An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data
FQAS '02 Proceedings of the 5th International Conference on Flexible Query Answering Systems
Cluster and Calendar Based Visualization of Time Series Data
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Visualizing Time-Series on Spirals
INFOVIS '01 Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS'01)
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Dynamic query tools for time series data sets: timebox widgets for interactive exploration
Information Visualization
Visually mining and monitoring massive time series
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Dot Plots for Time Series Analysis
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
VizTree: a tool for visually mining and monitoring massive time series databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Stock time series visualization based on data point importance
Engineering Applications of Artificial Intelligence
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Time series shapelets: a new primitive for data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving discovery of frequent patterns in time series
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
Piecewise cloud approximation for time series mining
Knowledge-Based Systems
Improving the classification accuracy of streaming data using SAX similarity features
Pattern Recognition Letters
Representing unevenly-spaced time series data for visualization and interactive exploration
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
Shape-based template matching for time series data
Knowledge-Based Systems
A method of similarity measure and visualization for long time series using binary patterns
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
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Time series visualization is one of the most fundamental tasks, which is often used to discover patterns by a user interface. Many increasing interests in time series visualization in the last decade have resulted in the development of various state-of-the-art visualization techniques. In most cases, time series visualization is based on one of the representation frameworks which not only reduce the dimensionality, but sufficiently reflect the shapes of the raw time series as well. In this paper, a new time series visualization based on shape features is proposed to discover surprising patterns and mine frequent trends (motifs discovery). Since the shape features validly summarize both the global and local structures of time series and often use the slopes (or the angles) of the changeable values over time to describe the trends of time series, part of the work is to extract the important shape features and transform them into symbol string. After dimensionality reduction and symbol representation, a circle plotted by the visualization technique is split into many small sectors which simultaneously represent substring patterns of unequal length by multi-resolution function. Since the overall shape features of time series are based on data point importance, the surprising patterns and frequent trends can be accurately discovered even under a high compress ratio. The refined results of patterns discovery and frequent trends mining on financial and other time series datasets indicate that the proposed approach is an effective visualization tool for time series mining.