What if the very foundation of how artificial intelligence generates language was about to change? For years, AI systems have relied on token-based models, carefully crafting sentences one word at a ...

To model potential structural shifts in the data that depend on their historical values, different smooth transition autoregressive models are constructed and compared for the changes in the ...

Bootstrap procedures for local projections typically rely on assuming that the data generating process (DGP) is a finite order vector autoregression (VAR), often taken to be that implied by the local ...

In a world where side hustles and passive income are the new status symbols, Launch Vector is turning heads with a lifestyle-friendly approach to e-commerce investing. The Wyoming-based company offers ...

In the current macroeconomic environment characterized by complex interactions between monetary policy, labor markets, and price dynamics, traditional single-equation models fail to capture the ...

Abstract: Dynamic multi-objective optimisation (DMO) handles optimisation problems with multiple (often conflicting) objectives in varying environments. This paper proposes vector autoregressive ...

Vector Autoregression (VAR) is a statistical model used to capture the linear interdependencies among multiple time series data. It generalizes the univariate autoregressive model to multiple time ...

On Thursday, Inception Labs released Mercury Coder, a new AI language model that uses diffusion techniques to generate text faster than conventional models. Unlike traditional models that create text ...

ABSTRACT: To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models.

Vector winds play a crucial role in triggering and maintaining convective weather, such as squall lines and typhoons, as well as the effective utilization of wind energy resources. The mesoscale model ...