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...
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 presents a novel framework for applying semantic labels to events within a track. A track is a two-dimensional (2D) or a three-dimensional (3D) signal in time where eac...
In many applications, we need to analyze a large number of time series. Segments of time series demonstrating dominating advantages over others are often of particular interest. In...