A qualitative physics based on confluences
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Commonsense reasoning about causality: deriving behavior from structure
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Learning probabilistic automata with variable memory length
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Time series prediction using belief network models
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
On the learnability and usage of acyclic probabilistic finite automata
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Distance and Feature-Based Clustering of Time Series: An Application on Neurophysiology
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Supporting clinico-genomic knowledge discovery: a multi-strategy data mining process
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
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Two methods to assign discrete values to continuous values from time series, using dynamic information about the series, are proposed. The first method is based on a particular statistic which allows us to select a discrete value for a new continuous value from the series. The second one is based on a concept of significant distance between consecutive values from time series which is defined. This definition is based on qualitative changes in the time series values. In both methods, the conversion process of continuous values into discrete values is dynamic in opposition to static classical methods used in machine learning. Finally, we use the proposed methods in a practical case. We transform the daily clearness index time series into discrete values. The results display that the series with discrete values obtained from the dynamic process captures better the sequential properties of the original continuous series.