Stock price forecasting is a challenging task due to the complexity of financial markets and
the high volatility of stocks. Because of the strong nonlinear representation ability of neural …

Precise demand forecasting and agile pricing strategies are crucial in modern business.
This study aims to enhance these strategies by evaluating the efficacy of Holt-Winters …

Spatial and spatiotemporal volatility models are a class of models designed to capture
spatial dependence in the volatility of spatial and spatiotemporal data. Spatial dependence …

This paper explores the role of network spillovers in commodity market forecasting and
proposes a novel factor-augmented dynamic network model. We focus on a novel network …

An accurate estimation of future stock prices can help investors maximize their profits. The
current advancements in the area of Artificial Intelligence (AI) have proven prevalent in the …

We introduce a dynamic spatiotemporal volatility model that extends traditional approaches
by incorporating spatial, temporal, and spatiotemporal spillover effects, along with volatility …

Accurate stock market prediction provides great opportunities for informed decision-making,
yet existing methods struggle with financial data's non-linear, high-dimensional, and volatile …

Under the increasing electricity consumption trend and complex power consumption forms,
accurate power load forecasting faces severe challenges. This paper proposes the …

Stock market indices are volatile by nature, and sudden shocks are known to affect volatility
patterns. The autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH …

This paper provides two specification tests for the system of spatial autoregressive model of
order m. We derive the theoretical limit distributions and show in a detailed Monte Carlo …