  In the online (time-series) search problem, a player is presented with a
sequence of prices which are revealed in an online manner. In the standard
definition of the problem, for each revealed price, the player must decide
irrevocably whether to accept or reject it, without knowledge of future prices
(other than an upper and a lower bound on their extreme values), and the
objective is to minimize the competitive ratio, namely the worst-case ratio
between the maximum price in the sequence and the one selected by the player.
The problem formulates several applications of decision-making in the face of
uncertainty on the revealed samples.
  Previous work on this problem has largely assumed extreme scenarios in which
either the player has almost no information about the input, or the player is
provided with some powerful, and error-free advice. In this work, we study
learning-augmented algorithms, in which there is a potentially erroneous
prediction concerning the input. Specifically, we consider two different
settings: the setting in which the prediction is related to the maximum price
in the sequence, as well as the setting in which the prediction is obtained as
a response to a number of binary queries. For both settings, we provide tight,
or near-tight upper and lower bounds on the worst-case performance of search
algorithms as a function of the prediction error. We also provide experimental
results on data obtained from stock exchange markets that confirm the
theoretical analysis, and explain how our techniques can be applicable to other
learning-augmented applications.
