  \textit{SummerTime} seeks to summarize globally time series signals and
provides a fixed-length, robust summarization of the variable-length time
series. Many classical machine learning methods for classification and
regression depend on data instances with a fixed number of features. As a
result, those methods cannot be directly applied to variable-length time series
data. One common approach is to perform classification over a sliding window on
the data and aggregate the decisions made at local sections of the time series
in some way, through majority voting for classification or averaging for
regression. The downside to this approach is that minority local information is
lost in the voting process and averaging assumes that each time series
measurement is equal in significance. Also, since time series can be of varying
length, the quality of votes and averages could vary greatly in cases where
there is a close voting tie or bimodal distribution of regression domain.
Summarization conducted by the \textit{SummerTime} method will be a
fixed-length feature vector which can be used in-place of the time series
dataset for use with classical machine learning methods. We use Gaussian
Mixture models (GMM) over small same-length disjoint windows in the time series
to group local data into clusters. The time series' rate of membership for each
cluster will be a feature in the summarization. The model is naturally capable
of converging to an appropriate cluster count. We compare our results to
state-of-the-art studies in physical activity classification and show
high-quality improvement by classifying with only the summarization. Finally,
we show that regression using the summarization can augment energy expenditure
estimation, producing more robust and precise results.
