  Forecast of optical turbulence and atmospheric parameters relevant for
ground-based astronomy is becoming an important goal for telescope planning and
AO instruments optimization in several major telescope. Such detailed and
accurate forecast is typically performed with numerical atmospheric models.
Recently short-term forecasts (a few hours in advance) are also being provided
(ALTA project) using a technique based on an autoregression approach, as part
of a strategy that aims to increase the forecast accuracy. It has been proved
that such a technique is able to achieve unprecedented performances so far.
Such short-term predictions make use of the numerical model forecast and
real-time observations. In recent years machine learning (ML) techniques also
started to be used to provide an atmospheric and turbulence forecast.
Preliminary results indicate however an accuracy not really competitive with
respect to the autoregressive method or even prediction by persistence. This
technique might be applicable joint to atmospheric model. It is therefore
interesting to investigate the main features of their performances and
characteristics (also because there is a great number of algorithms potentially
accessible) to understand if results achieved so far can be further improved
using ML. In this study we focus on a purely machine learning application to
short term forecast (1-2 hours) of astroclimatic and other atmospheric
parameters above VLT.
