Time-to-Pattern: Information-Theoretic Unsupervised Learning for
  Scalable Time Series Summarization
Alireza Ghods Trong Nghia Hoang Diane Cook
Data summarization is the process of generating interpretable and
representative subsets from a dataset. Existing time series summarization
approaches often search for recurring subsequences using a set of manually
devised similarity functions to summarize the data. However, such approaches
are fraught with limitations stemming from an exhaustive search coupled with a
heuristic definition of series similarity. Such approaches affect the diversity
and comprehensiveness of the generated data summaries. To mitigate these
limitations, we introduce an approach to time series summarization, called
Time-to-Pattern (T2P), which aims to find a set of diverse patterns that
together encode the most salient information, following the notion of minimum
description length. T2P is implemented as a deep generative model that learns
informative embeddings of the discrete time series on a latent space
specifically designed to be interpretable. Our synthetic and real-world
experiments reveal that T2P discovers informative patterns, even in noisy and
complex settings. Furthermore, our results also showcase the improved
performance of T2P over previous work in pattern diversity and processing
scalability, which conclusively demonstrate the algorithm's effectiveness for
time series summarization.