Abstract— We present a method for performing mode classification of real-time streams of GPS surface position data. Our approach has two parts: an algorithm for robust, unconstr...
Robert Granat, Galip Aydin, Marlon E. Pierce, Zhig...
In this work, we propose to use the Zoomed-Ranking approach to ranking and selecting Artificial Neural Network (ANN) models for time series forecasting. Given a time series to fo...
Effective similarity search in multi-media time series such as video or audio sequences is important for content-based multi-media retrieval applications. We propose a framework th...
The last decade has seen a huge interest in classification of time series. Most of this work assumes that the data resides in main memory and is processed offline. However, recent...
Shashwati Kasetty, Candice Stafford, Gregory P. Wa...
A novel approach to measure the interdependence of time series is proposed, based on the alignment (“matching”) of their Huang-Hilbert spectra. The method consists of three st...
Abstract. Recently, we have proposed a novel method for the compression of time series based on mathematical models that explore dependencies between different time series. This r...
Clustering time series is usually limited by the fact that the length of the time series has a significantly negative influence on the runtime. On the other hand, approximative c...
Effort to evolve and maintain a software system is likely to vary depending on the amount and frequency of change requests. This paper proposes to model change requests as time se...
— In this paper we present a structure theory for generalized linear dynamic factor models (GDFM’s). Emphasis is laid on the so-called zeroless case. GDFM’s provide a way of ...
The analysis of gene expression time series obtained from microarray experiments can be effectively exploited to understand a wide range of biological phenomena from the homeostat...