Temporal Machine Learning Research
Last modified: 18 March 2001.
Reliable and robust classification techniques have been
developed for static domains for several years now. However,
recently, there has been increased interest in classification,
clustering, searching and other processing of things that vary
over time. These include things like sensor information from
robots, signals from biomedical sources like
electrocardiographs, financial markets, gesture and more.
The goal is to find patterns in data that varies over time.
This page lists the researchers that I know about that work in
the area. If you have any suggestions, people I've missed, etc.,
I would really appreciate it if you would contact me.
Dimensions of research
There are a variety of different sub-problems within this
domain. The main dimensions of these variations are:
- Supervised vs Unsupervised: The temporal
data can be labelled, telling us what type of pattern is, or
alternatively the goal can be more difficult, along the
lines of finding interesting and recurring patterns.
- Univariate vs Multivariate: Is a single
"channel" of data being analysed, or is it a domain where
there is more than one source of data that is simultaneously
Reinventing the wheel?
Time series have also been analysed for a long time before
machine learning researchers began taking a closer look at
it. In view of this, it is especially important to avoid
reinventing the wheel constantly. This section highlights some
of the more established areas that correspond to parts of the
temporal machine learning problem:
- Hidden Markov Models
- Recurrent Neural Networks
- Allen's Interval Logic
- Knowledge-based signal processing
Gautam Das is
doing work in finding similar time series.
Fisher is looking at techniques for clustering time
series. In addition, two of his students have worked or are
working on the problem:
Manganaris has built a system for classifying univariate
data streams for his PhD. His PhD also has a good literature
survey for getting into the area.
Talbert is looking at clustering temporal structures.
Paliouras did a PhD thesis on refining temporal
recognition expert systems. He applied his techniques to whale
songs. Also has a good literature survey, especially on
knowledge-based signal processing.
Eamonn Keogh is
looking at ways of picking up patterns from temporal data.
Mannila is interested in sequence data (like network
logs) as well as time series (ECGs and the like). He has a
wide range of papers available in the area.
- Padhraic Smyth
also does some work in the area, as well a great deal of work
on a lot of other topics related to time series analysis.
Waleed Kadous is developing a multivariate
- Jeffrey D. Scargle is working on techniques for analysing time series from astronomical sources.
Hoeppner on the qualitative aspects of time series used by
humans in analysing time series.
Related Conferences & Workshops
If you have any suggestions, corrections, etc., please don't
hesitate to e-mail me at firstname.lastname@example.org
Mohammed Waleed Kadous
Last modified: Mon May 7 23:01:29 EST 2001