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Proceedings Paper

A multiscale statistical model for time series forecasting
Author(s): W. Wang; I. Pollak
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Paper Abstract

We propose a stochastic grammar model for random-walk-like time series that has features at several temporal scales. We use a tree structure to model these multiscale features. The inside-outside algorithm is used to estimate the model parameters. We develop an algorithm to forecast the sign of the first difference of a time series. We illustrate the algorithm using log-price series of several stocks and compare with linear prediction and a neural network approach. We furthermore illustrate our algorithm using synthetic data and show that it significantly outperforms both the linear predictor and the neural network. The construction of our synthetic data indicates what types of signals our algorithm is well suited for.

Paper Details

Date Published: 3 March 2007
PDF: 9 pages
Proc. SPIE 6498, Computational Imaging V, 649815 (3 March 2007); doi: 10.1117/12.722198
Show Author Affiliations
W. Wang, Purdue Univ. (United States)
I. Pollak, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 6498:
Computational Imaging V
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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