  Suppose that we observe a short time series where each time-t-specific
data-structure consists of many slightly dependent data indexed by a and that
we want to estimate a feature of the law of the experiment that depends neither
on t nor on a. We develop and study an algorithm to learn sequentially which
base algorithm in a user-supplied collection best carries out the estimation
task in terms of excess risk and oracular inequalities. The analysis, which
uses dependency graph to model the amount of conditional independence within
each t-specific data-structure and a concentration inequality by Janson [2004],
leverages a large ratio of the number of distinct a's to the degree of the
dependency graph in the face of a small number of t-specific data-structures.
The so-called one-step ahead Super Learner is applied to the motivating example
where the challenge is to anticipate the cost of natural disasters in France.
