  This paper describes a flexible approach to short term prediction of
meteorological variables. In particular, we focus on the prediction of the
solar irradiance one hour ahead, a task that has high practical value when
optimizing solar energy resources. As D\'efi EGC 2018 provides us with time
series data for multiple sensors (e.g. solar irradiance, temperature,
hygrometry), recorded every minute for two years and 5 geographical sites from
La R\'eunion island, we test the value of using recently observed data as input
for prediction models, as well as the performance of models across sites. After
describing our data cleaning and normalization process, we combine a variable
selection step based on AutoRegressive Integrated Moving Average (ARIMA)
models, to using general purpose regression techniques such as neural networks
and regression trees.
