Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast time-series searching with scaling and shifting
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Personal and Ubiquitous Computing
Querying Time Series Data Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Variable Length Queries for Time Series Data
Proceedings of the 17th International Conference on Data Engineering
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Similarity Search Over Time-Series Data Using Wavelets
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Incremental stock time series data delivery and visualization
Proceedings of the 14th ACM international conference on Information and knowledge management
Financial prediction and trading strategies using neurofuzzyapproaches
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonstationary time series analysis by temporal clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Knowledge discovery in time series databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Joint segmentation and classification of time series using class-specific features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Finding relevant sequences in time series containing crisp, interval, and fuzzy interval data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A method for segmentation of switching dynamic modes in time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Time series forecasting with a hybrid clustering scheme and pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Time Series Forecasting of Averaged Data With Efficient Use of Information
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Representing financial time series based on important extrema points
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
A review on time series data mining
Engineering Applications of Artificial Intelligence
TSX: a novel symbolic representation for financial time series
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
A representation of time series based on implicit polynomial curve
Pattern Recognition Letters
OBST-based segmentation approach to financial time series
Engineering Applications of Artificial Intelligence
Stock market co-movement assessment using a three-phase clustering method
Expert Systems with Applications: An International Journal
Trend forecasting of financial time series using PIPs detection and continuous HMM
Intelligent Data Analysis
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Recently, the increasing use of time series data has initiated various research and development attempts in the field of data and knowledge management. Time series data is characterized as large in data size, high dimensionality and update continuously. Moreover, the time series data is always considered as a whole instead of individual numerical fields. Indeed, a large set of time series data is from stock market. Stock time series has its own characteristics over other time series. Moreover, dimensionality reduction is an essential step before many time series analysis and mining tasks. For these reasons, research is prompted to augment existing technologies and build new representation to manage financial time series data. In this paper, financial time series is represented according to the importance of the data points. With the concept of data point importance, a tree data structure, which supports incremental updating, is proposed to represent the time series and an access method for retrieving the time series data point from the tree, which is according to their order of importance, is introduced. This technique is capable to present the time series in different levels of detail and facilitate multi-resolution dimensionality reduction of the time series data. In this paper, different data point importance evaluation methods, a new updating method and two dimensionality reduction approaches are proposed and evaluated by a series of experiments. Finally, the application of the proposed representation on mobile environment is demonstrated.