It is known that ramp-based models are not sufficient for accurate timing modeling. In this paper, we develop a technique that accurately models the waveforms, and also allows a f...
Anand Ramalingam, Ashish Kumar Singh, Sani R. Nass...
We propose Merge Growing Neural Gas (MGNG) as a novel unsupervised growing neural network for time series analysis. MGNG combines the state-of-the-art recursive temporal context of...
Andreas Andreakis, Nicolai von Hoyningen-Huene, Mi...
This paper presents an study about a new Hybrid method GRASPES - for time series prediction, inspired in F. Takens theorem and based on a multi-start metaheuristic for combinatori...
Aranildo Rodrigues Lima Junior, Tiago Alessandro E...
We study similarity queries for time series data where similarity is defined in terms of a set of linear transformations on the Fourier series representation of a sequence. We hav...
Abstract. During the last years, timed automata have become a popular model for describing the behaviour of real-time systems. In particular, there has been much research on proble...