  While Internet of Things (IoT) devices and sensors create continuous streams
of information, Big Data infrastructures are deemed to handle the influx of
data in real-time. One type of such a continuous stream of information is time
series data. Due to the richness of information in time series and inadequacy
of summary statistics to encapsulate structures and patterns in such data,
development of new approaches to learn time series is of interest. In this
paper, we propose a novel method, called pattern tree, to learn patterns in the
times-series using a binary-structured tree. While a pattern tree can be used
for many purposes such as lossless compression, prediction and anomaly
detection, in this paper we focus on its application in time series estimation
and forecasting. In comparison to other methods, our proposed pattern tree
method improves the mean squared error of estimation.
