Online estimation methods for irregular autoregressive models

TL;DR

This paper introduces and evaluates three online estimation algorithms—gradient descent, Newton-step, and Kalman filter—for irregular autoregressive models, demonstrating their efficiency and adaptability in processing large, irregularly sampled time series data.

Contribution

The paper develops and tests new online learning algorithms specifically for irregular autoregressive models, improving estimation speed and adaptability over traditional batch methods.

Findings

Algorithms provide accurate parameter estimates for regular and irregular data.

Online methods significantly reduce computational time.

They adapt quickly to changes in time series behavior.

Abstract

In the last decades, due to the huge technological growth observed, it has become increasingly common that a collection of temporal data rapidly accumulates in vast amounts. This provides an opportunity for extracting valuable information through the estimation of increasingly precise models. But at the same time it imposes the challenge of continuously updating the models as new data become available. Currently available methods for addressing this problem, the so-called online learning methods, use current parameter estimations and novel data to update the estimators. These approaches avoid using the full raw data and speeding up the computations. In this work we consider three online learning algorithms for parameters estimation in the context of time series models. In particular, the methods implemented are: gradient descent, Newton-step and Kalman filter recursions. These algorithms are applied to the recently developed irregularly observed autoregressive (iAR) model. The estimation accuracy of the proposed methods is assessed by means of Monte Carlo experiments. The results obtained show that the proposed online estimation methods allow for a precise estimation of the parameters that generate the data both for the regularly and irregularly observed time series. These online approaches are numerically efficient, allowing substantial computational time savings. Moreover, we show that the proposed methods are able to adapt the parameter estimates quickly when the time series behavior changes, unlike batch estimation methods.