  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.
