Iterative Forgetting: Online Data Stream Regression Using
  Database-Inspired Adaptive Granulation
Niket Kathiriya Hossein Haeri Cindy Chen Kshitij Jerath
Many modern systems, such as financial, transportation, and
telecommunications systems, are time-sensitive in the sense that they demand
low-latency predictions for real-time decision-making. Such systems often have
to contend with continuous unbounded data streams as well as concept drift,
which are challenging requirements that traditional regression techniques are
unable to cater to. There exists a need to create novel data stream regression
methods that can handle these scenarios. We present a database-inspired
datastream regression model that (a) uses inspiration from R*-trees to create
granules from incoming datastreams such that relevant information is retained,
(b) iteratively forgets granules whose information is deemed to be outdated,
thus maintaining a list of only recent, relevant granules, and (c) uses the
recent data and granules to provide low-latency predictions. The
R*-tree-inspired approach also makes the algorithm amenable to integration with
database systems. Our experiments demonstrate that the ability of this method
to discard data produces a significant order-of-magnitude improvement in
latency and training time when evaluated against the most accurate
state-of-the-art algorithms, while the R*-tree-inspired granulation technique
provides competitively accurate predictions