Empirical study of periodic autoregressive models with additive noise -- estimation and testing

TL;DR

This paper introduces four new estimation techniques for periodic autoregressive models affected by additive noise, demonstrating their effectiveness through simulations and real data applications, especially under extreme noise conditions.

Contribution

It proposes novel Yule-Walker based estimation methods for noise-corrupted PAR models and develops a testing procedure to identify model suitability in noisy environments.

Findings

Proposed techniques perform well even with significant additive noise.

Simulation studies confirm the efficiency of the methods under various noise conditions.

Applied the testing procedure successfully to real air particulate matter data.

Abstract

Periodic autoregressive (PAR) time series with finite variance is considered as one of the most common models of second-order cyclostationary processes. However, in the real applications, the signals with periodic characteristics may be disturbed by additional noise related to measurement device disturbances or to other external sources. Thus, the known estimation techniques dedicated for PAR models may be inefficient for such cases. When the variance of the additive noise is relatively small, it can be ignored and the classical estimation techniques can be applied. However, for extreme cases, the additive noise can have a significant influence on the estimation results. In this paper, we propose four estimation techniques for the noise-corrupted PAR models with finite variance distributions. The methodology is based on Yule-Walker equations utilizing the autocovariance function. It can be used for any type of the finite variance additive noise. The presented simulation study clearly indicates the efficiency of the proposed techniques, also for extreme case, when the additive noise is a sum of the Gaussian additive noise and additive outliers. The proposed estimation techniques are also applied for testing if the data corresponds to noise-corrupted PAR model. This issue is strongly related to the identification of informative component in the data in case when the model is disturbed by additive non-informative noise. The power of the test is studied for simulated data. Finally, the testing procedure is applied for two real time series describing particulate matter concentration in the air.