Diffusion models, a family of generative models based on deep learning, have become
increasingly prominent in cutting-edge machine learning research. With distinguished …

The challenges in efficient data representation and similarity measures on massive amounts
of time series have enormous impact on many applications. This paper addresses an …

We propose in this paper a novel trend based SAX reduction technique called SAX_CP,
which captures the trends in a time series based on abrupt change points and on data …

A similarity measure is one of the most important tasks in the fields of time series data
mining. Its quality often affects the efficiency and effectiveness of the related algorithms that …

Numerous dimensionality-reducing representations of time series have been proposed in
data mining and have proved to be useful, especially in handling a high volume of time …

Time-series representation is the most important task in time-series analysis. One of the
most widely employed time-series representation method is symbolic aggregate …

Symbolic Aggregate approXimation (SAX) is one of the most common dimensionality
reduction approaches for time-series and has been widely employed in lots of domains …

In this work, we introduce the Slopewise Aggregate Approximation (SAA), an innovative
variation of the Piecewise Aggregate Approximation. The Slopewise Aggregate …

In the area of time series data mining, a challenging task is to design an effectively and
efficiently low-dimensional representation of high-dimensional time series data. Such an …

There are various types of autonomous unmanned systems, covering different spaces of
sea, land, and air, and they are comprehensively going deep into multiple fields of national …