Convergence of the BFGS Method for LC1 Convex Constrained Optimization
SIAM Journal on Control and Optimization
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neural network design
Nonlinear time series analysis
Nonlinear time series analysis
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Neural Networks
Making Subsequence Time Series Clustering Meaningful
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Making clustering in delay-vector space meaningful
Knowledge and Information Systems
NOLASC'05 Proceedings of the 4th WSEAS International Conference on Non-linear Analysis, Non-linear Systems and Chaos
Expert Systems with Applications: An International Journal
Spatiotemporal analysis in virtual environments using eigenbehaviors
Proceedings of the 7th International Conference on Advances in Computer Entertainment Technology
Applications of data mining time series to power systems disturbance analysis
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Matching Observed with Empirical Reality --What you see is what you get?
Fundamenta Informaticae - Dedicated to the Memory of Professor Manfred Kudlek
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The new time series data mining framework proposed in this paper applies Reconstructured Phase Space (RPS) to identify temporal patterns that are characteristic and predictive of significant events in a complex time series. The new framework utilizes the fuzzy set and the Gaussian-shaped membership function to define temporal patterns in the time-delay embedding phase space. The resulting objective function represents not only the overall value of the event function, but also the weight of the vector in the temporal pattern cluster to which it contributes. Also, the new objective function is continuously differentiable so the gradient descent optimization such as quasi-Newton's method can be applied to search the optimal temporal patterns with much faster speed of convergence. The computational stability is significantly improved over the genetic algorithm originally used in our early framework. A new simple but effective two-step optimization strategy is proposed which further improves the search performance. Another significant contribution is the use of mutual information and false neighbors methods to estimate the time delay and the phase space dimension. We also implemented two experimental applications to demonstrate the effectiveness of the new framework with comparisons to the original framework and to the neural network prediction approach.