1 citations · 2 across the 2 of their papers we have counts for
4 papers · 2 filters
Can automated smoothing significantly improve benchmark time series classification algorithms?
James Large, Paul Southam, Anthony Bagnall
tl;dr: no, it cannot, at least not on average on the standard archive problems. We assess whether using six smoothing algorithms (moving average, exponential smoothing, Gaussian fi…
The UEA multivariate time series classification archive, 2018
Anthony Bagnall, Hoang Anh Dau, Jason Lines +5
In 2002, the UCR time series classification archive was first released with sixteen datasets. It gradually expanded, until 2015 when it increased in size from 45 datasets to 85 dat…
From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers
James Large, Anthony Bagnall, Simon Malinowski +1
A family of algorithms for time series classification (TSC) involve running a sliding window across each series, discretising the window to form a word, forming a histogram of word…
Is rotation forest the best classifier for problems with continuous features?
A. Bagnall, M. Flynn, J. Large +3
In short, our experiments suggest that yes, on average, rotation forest is better than the most common alternatives when all the attributes are real-valued. Rotation forest is a tr…