This paper describes a novel approach to representing experimental biological data in metabolic networks. The aim is to allow biologists to visualise and analyse the data in the c...
Time series data abounds in real world problems. Measuring the similarity of time series is a key to solving these problems. One state of the art measure is the longest common sub...
We present a method for unsupervised discovery of abnormal occurrences of activities in multi-dimensional time series data. Unsupervised activity discovery approaches differ from ...
The problem of locating motifs in real-valued, multivariate time series data involves the discovery of sets of recurring patterns embedded in the time series. Each set is composed...
David Minnen, Charles Lee Isbell Jr., Irfan A. Ess...
The novel Time Series Data Mining (TSDM) framework is applied to analyzing financial time series. The TSDM framework adapts and innovates data mining concepts to analyzing time ser...
Abstract-- Automatic recognition of activities using time series data collected from exercise can facilitate development of applications that motivate people to exercise more frequ...
Pekka Siirtola, Perttu Laurinen, Eija Haapalainen,...
There has been much recent interest in retrieval of time series data. Earlier work has used a fixed similarity metric (e.g., Euclidean distance) to determine the similarity betwee...
Segmentation is a popular technique for discovering structure in time series data. We address the largely open problem of estimating the number of segments that can be reliably di...
A visualisation system that deals with two modalities of information – numerical and textual – is presented. The current application domain is that of prediction in financial ...
In this work we present an improved evolutionary method for inferring S-system model of genetic networks from the time series data of gene expression. We employed Differential Ev...