Computationally Efficient Stochastic Realization for Internal Multiscale Autoregressive Models
Multidimensional Systems and Signal Processing
Fuzzy multiresolution signal representation
Fuzzy Sets and Systems
Decision Support Systems - Special issue: Data mining for financial decision making
An incident analysis system NICTER and its analysis engines based on data mining techniques
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
DCOSS'05 Proceedings of the First IEEE international conference on Distributed Computing in Sensor Systems
| Hi-index | 754.84 |
The multiscale autoregressive (MAR) framework was introduced to support the development of optimal multiscale statistical signal processing. Its power resides in the fast and flexible algorithms to which it leads. While the MAR framework was originally motivated by wavelets, the link between these two worlds has been previously established only in the simple case of the Haar wavelet. The first contribution of this paper is to provide a unification of the MAR framework and all compactly supported wavelets as well as a new view of the multiscale stochastic realization problem. The second contribution of this paper is to develop wavelet-based approximate internal MAR models for stochastic processes. This will be done by incorporating a powerful synthesis algorithm for the detail coefficients which complements the usual wavelet reconstruction algorithm for the scaling coefficients. Taking advantage of the statistical machinery provided by the MAR framework, we will illustrate the application of our models to sample-path generation and estimation from noisy, irregular, and sparse measurements