Modeling Periodic Functions for Time Series Analysis

Anish Biswas

Time series problems involve analysis of periodic functions for predicting the future. A flexible regression method should be able to dynamically select the appropriate model to fit the available data. In this paper, we present a function approximation scheme that can be used for modeling periodic functions using a series of orthogonal polynomials, named Chebychev polynomials. In our approach, we obtain an estimate of the error due to neglecting higher order polynomials and thus can flexibly select a polynomial model of the proper order. We also show that this approximation approach is stable in the presence of noise.

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.