=Paper=
{{Paper
|id=Vol-2738/keynote1
|storemode=property
|title=Time Series Classification at Scale
|pdfUrl=https://ceur-ws.org/Vol-2738/keynote1.pdf
|volume=Vol-2738
|authors=Geoffrey I. Webb
|dblpUrl=https://dblp.org/rec/conf/lwa/Webb20
}}
==Time Series Classification at Scale==
<pdf width="1500px">https://ceur-ws.org/Vol-2738/keynote1.pdf</pdf>
<pre>
          Time Series Classification at Scale

                                Geoffrey I. Webb

  Faculty of Information Technology, Monash University, VIC 3800, Australia
                           Geoff.Webb@monash.edu



    Abstract. Time series classification is a fundamental data science prob-
    lem, providing understanding of dynamic processes as they evolve over
    time. The recent introduction of ensemble techniques has revolutionised
    this field, greatly increasing accuracy, but at a cost of increasing already
    burdensome computational overheads. I present new time series classifi-
    cation technologies that achieve the same accuracy as recent state-of-the-
    art developments, but with many orders of magnitude greater efficiency
    and scalability. These make time series classification feasible at hitherto
    unattainable scale.




Copyright ©   2020 by the paper’s authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).

</pre>