  Time Series Classification (TSC) has seen enormous progress over the last two
decades. HIVE-COTE (Hierarchical Vote Collective of Transformation-based
Ensembles) is the current state of the art in terms of classification accuracy.
HIVE-COTE recognizes that time series data are a specific data type for which
the traditional attribute-value representation, used predominantly in machine
learning, fails to provide a relevant representation. HIVE-COTE combines
multiple types of classifiers: each extracting information about a specific
aspect of a time series, be it in the time domain, frequency domain or
summarization of intervals within the series. However, HIVE-COTE (and its
predecessor, FLAT-COTE) is often infeasible to run on even modest amounts of
data. For instance, training HIVE-COTE on a dataset with only 1,500 time series
can require 8 days of CPU time. It has polynomial runtime with respect to the
training set size, so this problem compounds as data quantity increases. We
propose a novel TSC algorithm, TS-CHIEF (Time Series Combination of
Heterogeneous and Integrated Embedding Forest), which rivals HIVE-COTE in
accuracy but requires only a fraction of the runtime. TS-CHIEF constructs an
ensemble classifier that integrates the most effective embeddings of time
series that research has developed in the last decade. It uses tree-structured
classifiers to do so efficiently. We assess TS-CHIEF on 85 datasets of the
University of California Riverside (UCR) archive, where it achieves
state-of-the-art accuracy with scalability and efficiency. We demonstrate that
TS-CHIEF can be trained on 130k time series in 2 days, a data quantity that is
beyond the reach of any TSC algorithm with comparable accuracy.
