This paper presents the first time series clustering benchmark utilizing all time series
datasets currently available in the University of California Riverside (UCR) archive—the …

With the development of data-driven models, extracting information from historical data for
better energy forecasting is critically important for energy planning and optimization in …

Day-ahead load forecasting plays a crucial role in operation and management of power
systems. Weather conditions have a significant impact on daily load profile, hence, it follows …

Time series, as one of the most fundamental representations of sequential data, has been
extensively studied across diverse disciplines, including computer science, biology …

In this paper, companies listed on the Borsa Istanbul (BIST) Sustainability Index are
analyzed by performing a cluster analysis based on their environmental, social, and …

Granular computing has been an intense research area over the past two decades, focusing
on acquiring, processing, and interpreting information granules. In this study, we focus on …

Software-defined wireless mesh networks are being increasingly deployed in diverse
settings, such as smart cities and public Wi-Fi access infrastructures. The signal propagation …

Massive electrical load exhibits many patterns making it difficult for forecast algorithms to
generalise well. Most learning algorithms produce a better forecast for dominant patterns in …

This work exposes an innovative clustering method of solar radiation stations, using static
and dynamic parameters, based on multi-criteria analysis for future objectives to make the …

Stablecoins recently emerged as a solution to mitigate cryptocurrency's volatility while
preserving their advantages. Nowadays, various types of stablecoins with different protocol …