  In retail, there are predictable yet dramatic time-dependent patterns in
customer behavior, such as periodic changes in the number of visitors, or
increases in customers just before major holidays. The current paradigm of
multi-armed bandit analysis does not take these known patterns into account.
This means that for applications in retail, where prices are fixed for periods
of time, current bandit algorithms will not suffice. This work provides a
remedy that takes the time-dependent patterns into account, and we show how
this remedy is implemented for the UCB, $\varepsilon$-greedy, and UCB-L
algorithms, and also through a new policy called the variable arm pool
algorithm. In the corrected methods, exploitation (greed) is regulated over
time, so that more exploitation occurs during higher reward periods, and more
exploration occurs in periods of low reward. In order to understand why regret
is reduced with the corrected methods, we present a set of bounds that provide
insight into why we would want to exploit during periods of high reward, and
discuss the impact on regret. Our proposed methods perform well in experiments,
and were inspired by a high-scoring entry in the Exploration and Exploitation 3
contest using data from Yahoo$!$ Front Page. That entry heavily used
time-series methods to regulate greed over time, which was substantially more
effective than other contextual bandit methods.
