Dimensionality reduction for similarity searching in dynamic databases
SIGMOD '98 Proceedings of the 1998 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
Relevance feedback retrieval of time series data
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Locally adaptive dimensionality reduction for indexing large time series databases
ACM Transactions on Database Systems (TODS)
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
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Evolutionary Time Series Segmentation for Stock Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient Searches for Similar Subsequences of Different Lengths in Sequence Databases
ICDE '00 Proceedings of the 16th 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
Fast similarity search in the presence of longitudinal scaling in time series databases
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Online event-driven subsequence matching over financial data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A spatio-temporal extension to Isomap nonlinear dimension reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Local distance preservation in the GP-LVM through back constraints
ICML '06 Proceedings of the 23rd international conference on Machine learning
Stock time series pattern matching: Template-based vs. rule-based approaches
Engineering Applications of Artificial Intelligence
Intelligent stock trading system by turning point confirming and probabilistic reasoning
Expert Systems with Applications: An International Journal
IEEE Transactions on Computers
Representing financial time series based on data point importance
Engineering Applications of Artificial Intelligence
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Pattern recognition in stock data based on a new segmentation algorithm
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
A real time hybrid pattern matching scheme for stock time series
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
A review on time series data mining
Engineering Applications of Artificial Intelligence
Temporal Extension of Laplacian Eigenmaps for Unsupervised Dimensionality Reduction of Time Series
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Online Segmentation of Time Series Based on Polynomial Least-Squares Approximations
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Financial time series data are large in size and dynamic and non-linear in nature. Segmentation is often performed as a pre-processing step for locating technical patterns in financial time series. In this paper, we propose a segmentation method based on Turning Points (TPs). The proposed method selects TPs from the financial time series in question based on their degree of importance. A TP's degree of importance is calculated on the basis of its contribution to the preservation of the trends and shape of the time series. Algorithms are also devised to store the selected TPs in an Optimal Binary Search Tree (OBST) and to reconstruct the reduced sample time series. Comparison with existing approaches show that the time series reconstructed by the proposed method is able to maintain the shape of the original time series very well and preserve more trends. Our approach also ensures that the average retrieval cost is kept at a minimum.