Many other researchers in the area of artificial intelligence have also been looking at areas related to time. These have included work on things such as sequence prediction (e.g. Dietterich and Michalski's work with Eleusis [DM86]), work with temporal logics and their applications to recognising events, for example Kumar's work on temporal event conceptualisation [KM87] and the recent work in context detection and extraction for machine learning applications [Wid96, HHS98]. While some of these areas bear some interesting relations to temporal classification, they differ in several regards. Sequence prediction is not about learning from labelled examples, nor, typically does it deal with multivariate data. The event conceptualisation work focuses on recognition of temporal events, but not learning the events themselves. The work on context detection is about selecting which static classifier to use on a dynamic basis; whereas temporal classification is about classifying the dynamics themselves.
Increasingly, this area has become a popular research topic. For example, a workshop held at AAAI '98 [Dan98] while focusing on temporal prediction, also contained several papers on learning from time series. For example, Keogh and Pazzani [KP98] looks at automated ways of clustering time series from ECG signals and Shuttle information, by using a piecewise model combined with segmentation and agglomerative clustering. In Oates et al. [OJC98], a system is applied to extracting patterns from network failures, by looking at all possible sequences of events and keeping tabs on the frequency of these events.
Shahar [SM95] suggests an expert system architecture for knowledge-based temporal abstraction and also suggests that this system could be used for learning, though he does not actually do so. He then applies the techniques to clinical domains. Paliouras [Pal97] discusses refinement of temporal parameters in an existing temporal expert system. Manganaris [Man97] developed a system for supervised classification of univariate signals using piecewise polynomial modelling combined with a scale-space analysis technique (i.e. a technique that allows the system to cope with the problem that patterns occur at different scales) and applies them to space shuttle data as well as an artificial dataset.
Mannila et al [MTV95] have also been looking at temporal classification problems, in particular applying it to network traffic analysis. In their model, events are modelled not as a set of channels, but as a sequence of time-labelled events. Learning is accomplished by trying to find sequences of events and the relevant time constraints that fit the data.
Das et al [DLM 
98] also worked on ways of extracting rule
from univariate data trying to extract rules of the form ``A is
followed by B''.
Rosenstein and Cohen [RC98] used delay coordinates (a representation where the state at time t-n is compared to its state at time t with n varied appropriately to give an appropriate ``delay portrait''). These delay portraits are then clustered to create new representations, but they tend to be sensitive to variations in delay and speed.
Another interesting development in recent years is the application of
dynamic Bayesian networks to temporal classification tasks. While they
are not specifically designed for temporal classification (they are
more commonly used for prediction, or for estimating current state
given the previous state estimate), Zweig and Russell
[ZR98] have applied it to the task of speech
recognition. The main problem with using dynamic Bayesian network is
that while algorithms for learning the parameters of a Bayes net are
well-advanced, learning the structure of Bayes nets has proved more
difficult
. Friedman et al. [FMR98] are
developing techniques for learning the structure of dynamic Bayes
networks; it remains to be seen whether these techniques can
be applied in temporal classification domains.