A generalized Levinson algorithm for covariance extension with application to multiscale autoregressive modeling

Internal multiscale autoregressive processes, stochastic realization, and covariance extension

Forecasting Gold Price Changes: Application of an Equipped Artificial Neural Network

The forecast of fluctuations and prices is the major concern in financial markets. Thus, developing an accurate and robust forecasting decision model is critically favorable to the investors. As gold has shown a special capability to smooth inflation fluctuations, governors use gold as a price controlling lever. Thus, more information about future gold price trends will help to make the firm de...

Multiscale Autoregressive Models and Wavelets: Extended Version

The multiscale autoregressive (MAR) framework was introduced to support the development of optimal multiscale statistical signal processing. Its power resides in the fast and exible 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 rst cont...

Periodic Levinson-Durbin algorithm for entropy maximization

One gives a recursive algorithm for the computation of the first and second order derivatives of the entropy of a periodic autoregressive process with respect to the autocovariances. It is an extension of the periodic LevinsonDurbin algorithm. The algorithm has been developed for use at one of the steps of an entropy maximization method developed by the authors. Numerical examples of entropy ma...

Multi-Scale Autoregressive Processes

In many applications (e.g. recognition of geophysical and biomedical signals and multiscale analysis of images), it is of interest to analyze and recognize phenomena occuring at different scales. The recently introduced wavelet transforms provide a time-and-scale decomposition of signals that offers the possibility of such analysis. At present, however, there is no corresponding statistical fra...