Forecasting environmental hazards is critical in preventing or building resilience to their
impacts on human communities and ecosystems. Environmental data science is an …

Several methods exist in classification literature to quantify the similarity between two time
series data sets. Applications of these methods range from the traditional Euclidean-type …

The number of mass shootings in the United States has increased in the recent decades.
Understanding the future risk of the mass shootings is critical for designing strategies to …

Count time series data with excess zeros are observed in several applied disciplines. When
these zero-inflated counts are sequentially recorded, they might result in serial dependence …

In this paper, we propose a novel approach for classifying incoming continuous data under a
non-stationary environment. A class of estimators termed stochastic learning weak …

Crypto assets have lately become the chief interest of investors around the world. The
excitement around, along with the promise of the nascent technology led to enormous …

In computer science, stochastic processes, and industrial engineering, stationarity is often
taken to imply a stable, predictable flow of events and non-stationarity, consequently, a …

Hawaiian volcanoes such as Kilauea and Mauna Loa have drawn the attention of
researchers for quite some time and numerous theories abound hinting at a possible inverse …

Abstract Hidden Markov models (HMMs), especially those with a Poisson density governing
the latent state-dependent emission probabilities, have enjoyed substantial and undeniable …

Inferring intrinsic predictability (IP) or predictability limit (PL) from time series plays a crucial
role in understanding complex systems and guiding predictions. Though PL is often …