Abstract SAX (Symbolic Aggregate approXimation) is one of the main symbolization
techniques for time series. A well-known limitation of SAX is that trends are not taken into …

Abstract Symbolic Aggregate Approximation (SAX) is a widely used method for time series
data analysis, known for its ability to transform continuous data to discrete symbols. While …

Long time-series, involving thousands or even millions of time steps, are common in many
application domains but remain very difficult to explore interactively. Often the analytical task …

This paper proposes a method that discovers various sequential patterns from sequential
data. The sequential data is a set of sequences. Each sequence is a row of item sets. Many …

We present a solution to the problem of reactive power compensation and harmonic
compensation in a general case when both the source voltages and the load currents are …

One reason for researching new biometric modalities is to improve the capabilities of
security systems against threats. Biometric modalities based on biomedical signals, in …

In Time Series Classification (TSC) problems, parametric extension of the longest common
subsequence (LCSS) using discrete derivative has proved its superiority to the classic LCSS …

Making sense is an everyday activity whenever you face the unknown, something you
cannot grasp immediately, or when you observe a set of potentially conflicting information …

In this paper, we present a 3D handwriting character recognition algorithm based on a
symbolic representation of angular velocity signal generated from Gyroscope sensor …

The objectives of this thesis are to define novel symbolic representations and distance
measures that are suited for time series that can be multivariate and non-stationary. In …