Computer Sciences & Mathematics Forum, Volume 11, Issue 1

Demand forecasting is different from traditional forecasting because it is a process of forecasting multiple time series collectively. It is challenging to implement models that can generalise and perform well while forecasting many time series altog...

Oil, as a key commodity in international markets, bears an importance for both producers and consumers. For oil-exporting countries, periodic fluctuations have a considerable impact on the economic status and the way monetary and fiscal policies shou...

Natural dynamical systems can often display various long-term behaviours, ranging from entirely predictable decaying states to unpredictable, chaotic regimes or, more interestingly, highly correlated and intricate states featuring emergent phenomena....

This study investigates the behavior of South African stock prices during divestment periods using mixture distributions. Divestment often triggers significant market reactions, necessitating a deeper understanding of stock return distributions in su...

In this paper, interpersonal coordination is studied by analyzing physiological synchronization between individuals. To this end, a four-phase protocol is proposed to collect biosignals from the participants in each dyad. Then, the time evolution of...

Two-stage least squares (2SLS) regression undergirds much of contemporary geospatial econometrics. Walk-forward validation in time-series forecasting constitutes a special instance of iterative local regression. Two-stage least squares and iterative...

Effective forecasting is vital in various domains as it supports informed decision-making and risk mitigation. This paper aims to improve the selection of appropriate forecasting methods for univariate time series. We propose a systematic categorizat...

Anomaly detection in time series data is crucial across various domains. Traditional methods often struggle with continuously evolving time series requiring adjustment, whereas large language models (LLMs) and multi-modal LLMs (MLLMs) have emerged as...

This paper applies hierarchical clustering and Hamming Distance to analyze the temporal trends of infectious diseases across different regions of Uzbekistan. By leveraging hierarchical clustering, we effectively group regions based on disease similar...

This paper proposes a family of first order bivariate integer-valued autoregressive (BINAR(1)) with Poisson Lindley innovations (BINAR(1)PL). The model parameters are estimated using the conditional maximum likelihood (CML) estimation approach. The p...