Scientists use two forms of knowledge in the construction of explanatory models: generalized entities and processes that relate them; and constraints that specify acceptable combi...
We introduce a framework for defining a distance on the (non-Euclidean) space of Linear Dynamical Systems (LDSs). The proposed distance is induced by the action of the group of o...
Most time series data mining algorithms use similarity search as a core subroutine, and thus the time taken for similarity search is the bottleneck for virtually all time series d...
Thanawin Rakthanmanon, Bilson J. L. Campana, Abdul...
This paper presents a new method that uses orthogonalized features for time series clustering and classification. To cluster or classify time series data, either original data or...
Sequential pattern mining is a crucial but challenging task in many applications, e.g., analyzing the behaviors of data in transactions and discovering frequent patterns in time se...
Time-series data is a common target for visual analytics, as they appear in a wide range of application domains. Typical tasks in analyzing time-series data include identifying cy...
Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This paper proposes an efficient method to generate th...
Nathan A. Barker, Chris J. Myers, Hiroyuki Kuwahar...
Time Series Data Server (TSDS) is a software package for implementing a server that provides fast supersetting, sub-setting, filtering, and uniform gridding of time series-like dat...
Robert S. Weigel, Doug M. Lindholm, A. Wilson, Jer...
Time series pattern mining (TSPM) finds correlations or dependencies in same series or in multiple time series. When the numerous instances of multiple time series data are associ...
Time series data is common in many settings including scientific and financial applications. In these applications, the amount of data is often very large. We seek to support pred...