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
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
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
Visualization of Linear Time-Oriented Data: A Survey
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 1 - Volume 1
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
Incremental stock time series data delivery and visualization
Proceedings of the 14th ACM international conference on Information and knowledge management
Visualizing and discovering non-trivial patterns in large time series databases
Information Visualization
Dot Plots for Time Series Analysis
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Stock time series pattern matching: Template-based vs. rule-based approaches
Engineering Applications of Artificial Intelligence
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
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
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
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
A review on time series data mining
Engineering Applications of Artificial Intelligence
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
A new SAX-GA methodology applied to investment strategies optimization
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
Time series visualization based on shape features
Knowledge-Based Systems
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Time series visualization is a fundamental task in most financial applications. A framework that can reduce the dimensionality of the time series data, sufficiently accurate so that it can capture the actual shape of the time series but, at the same time, salient points will not be smoothed out can take advantage of charting the raw time series data. On the other hand, it is preferable that the representation framework can handle the multi-resolution problem rather than reduce the dimension to a fixed level only. In this paper, a framework that represents and visualizes time series data based on data point importance is proposed. Furthermore, discovering frequently appearing and surprising patterns are non-trivial tasks in financial applications. A method for discovering patterns across different resolutions is proposed. The proposed method is based on a modified version of VizTree. By converting the time series to symbol string based on data point importance, the potential patterns with different lengths can be encoded in the VizTree for visual pattern discovery while the important points and the overall shape of the time series patterns can be preserved even under a high compression ratio. Various experiments were conducted to evaluate the performance of the proposed framework. One may find it particularly attractive in financial applications like stock data analysis.