  As one of the most well-known artificial feature sampler, the sliding window
is widely used in scenarios where spatial and temporal information exists, such
as computer vision, natural language process, data stream, and time series.
Among which time series is common in many scenarios like credit card payment,
user behavior, and sensors. General feature selection for features extracted by
sliding window aggregate calls for time-consuming iteration to generate
features, and then traditional feature selection methods are employed to rank
them. The decision of key parameter, i.e. the period of sliding windows,
depends on the domain knowledge and calls for trivial. Currently, there is no
automatic method to handle the sliding window aggregate features selection. As
the time consumption of feature generation with different periods and sliding
windows is huge, it is very hard to enumerate them all and then select them.
  In this paper, we propose a general framework using Markov Chain to solve
this problem. This framework is very efficient and has high accuracy, such that
it is able to perform feature selection on a variety of features and period
options. We show the detail by 2 common sliding windows and 3 types of
aggregation operators. And it is easy to extend more sliding windows and
aggregation operators in this framework by employing existing theory about
Markov Chain.
