The open-source software RRDtool and Cricket provide a solution to the problem of collecting, storing, and visualizing service network time series data for the real-time monitorin...
A common way to represent a time series is to divide it into shortduration blocks, each of which is then represented by a set of basis functions. A limitation of this approach, ho...
We consider the question of predicting nonlinear time series. Kernel Dynamical Modeling (KDM), a new method based on kernels, is proposed as an extension to linear dynamical model...
It has long been known that Dynamic Time Warping (DTW) is superior to Euclidean distance for classification and clustering of time series. However, until lately, most research has...
Similarity search in time series databases is an important research direction. Several methods have been proposed in order to provide algorithms for efficient query processing in t...
Maria Kontaki, Apostolos Papadopoulos, Yannis Mano...
Multiple realizations of continuous-valued time series from a stochastic process often contain systematic variations in rate and amplitude. To leverage the information contained i...
Jennifer Listgarten, Radford M. Neal, Sam T. Rowei...
Recently we have proposed an algorithm of constructing hierarchical neural network classifiers (HNNC), that is based on a modification of error back-propagation. This algorithm co...
S. A. Dolenko, Yu. V. Orlov, I. G. Persiantsev, Ju...
This paper demonstrates how the selection of Prediction Strategy is important in the Long-Term Prediction of Time Series. Two strategies are already used in the prediction purposes...
Novelty detection in time series is an important problem with application in different domains such as machine failure detection, fraud detection and auditing. An approach to this...
Adriano L. I. Oliveira, Fernando Buarque de Lima N...
: Temporal data mining is concerned with the analysis of temporal data and finding temporal patterns, regularities, trends, clusters in sets of temporal data. Wavelet transform pro...