A neural network with a case based dynamic window for stock trading prediction
Expert Systems with Applications: An International Journal
Cluster-based genetic segmentation of time series with DWT
Pattern Recognition Letters
Evolving hypernetwork models of binary time series for forecasting price movements on stock markets
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IEEE Transactions on Audio, Speech, and Language Processing
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
Journal of Systems and Software
Neuro-genetic system for stock index prediction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
Characterizing sensor datasets with multi-granular spatio-temporal intervals
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
How many reference patterns can improve profitability for real-time trading in futures market?
Expert Systems with Applications: An International Journal
Ubiquitous intelligent information push-delivery for personalized content recommendation
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
Trading team composition for the intraday multistock market
Decision Support Systems
Robust estimation of vector autoregression (VAR) models using genetic algorithms
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Variance-wise segmentation for a temporal-adaptive SAX
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Trend forecasting of financial time series using PIPs detection and continuous HMM
Intelligent Data Analysis
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Time series data, due to their numerical and continuous nature, are difficult to process, analyze, and mine. However, these tasks become easier when the data can be transformed into meaningful symbols. Most recent works on time series only address how to identify a given pattern from a time series and do not consider the problem of identifying a suitable set of time points for segmenting the time series in accordance with a given set of pattern templates (e.g., a set of technical patterns for stock analysis). However, the use of fixed-length segmentation is an oversimplified approach to this problem; hence, a dynamic approach (with high controllability) is preferable so that the time series can be segmented flexibly and effectively according to the needs of the users and the applications. In view of the fact that this segmentation problem is an optimization problem and evolutionary computation is an appropriate tool to solve it, we propose an evolutionary time series segmentation algorithm. This approach allows a sizeable set of pattern templates to be generated for mining or query. In addition, defining similarity between time series (or time series segments) is of fundamental importance in fitness computation. By identifying the perceptually important points directly from the time domain, time series segments and templates of different lengths can be compared and intuitive pattern matching can be carried out in an effective and efficient manner. Encouraging experimental results are reported from tests that segment both artificial time series generated from the combinations of pattern templates and the time series of selected Hong Kong stocks.