Clustering time series data using the popular subsequence (STS) technique has been widely used in the data mining and wider communities. Recently the conclusion was made that it i...
Abstract. This paper deals with the interpretation of biomedical multivariate time series for extracting typical scenarios. This task is known to be difficult, due to the temporal ...
After the generation of multimedia data turned digital, an explosion of interest in their data storage, retrieval, and processing has drastically increased. This includes videos, ...
Many physical and artificial phenomena can be described by time series. The prediction of such phenomenon could be as complex as interesting. There are many time series forecasti...
— Rapidly evolving businesses generate massive amounts of time-stamped data sequences and defy a demand for massively multivariate time series analysis. For such data the predict...
— This paper suggests a constructive fuzzy system modeling for time series prediction. The model proposed is based on Takagi-Sugeno system and it comprises two phases. First, a f...
Time series analysis is a promising approach to discover temporal patterns from time stamped, numeric data. A novel approach to apply time series analysis to discern temporal info...
Harvey P. Siy, Parvathi Chundi, Daniel J. Rosenkra...
Sensor networks have increased the amount and variety of temporal data available, requiring the definition of new techniques for data mining. Related research typically addresses...
Leonardo E. Mariote, Claudia Bauzer Medeiros, Rica...
The problem of finding unusual time series has recently attracted much attention, and several promising methods are now in the literature. However, virtually all proposed methods...
Dragomir Yankov, Eamonn J. Keogh, Umaa Rebbapragad...