Deformable Markov model templates for time-series pattern matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Approximate Queries and Representations for Large Data Sequences
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Minimum Message Length Segmentation
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Generalized Dimension-Reduction Framework for Recent-Biased Time Series Analysis
IEEE Transactions on Knowledge and Data Engineering
OBST-based segmentation approach to financial time series
Engineering Applications of Artificial Intelligence
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In trying to find the features and patterns within the stock time series, time series segmentation is often required as one of the fundamental components in stock data mining. In this paper, a new stock time series segmentation algorithm is proposed. This proposed segmentation method contributes to containing both the important data points and the primitive trends like uptrend and downtrend, while most of the current algorithms only contain one aspect of that. The proposed segmentation algorithm is more efficient and effective in reserving the trends and less complexity than those combined split-and-merge segmentation algorithm. The research result shows that patterns found by using the algorithm and prior to the transaction time impact the stock transaction price. Encouraging experiment is reported from the tests that certain patterns appear most frequently before the low transaction price occurrence.