In this paper a new state model is introduced, an adaptative state model in a binary temporal representation (ASMBRT) as well as its application in constructing an algorithmic trading system. The presented model uses the binary temporal representation, which allows for a precise analysis of...

Accurate time-series forecasting of energy consumption and photovoltaic (PV) production is essential for effective energy management and sustainability. Deep Neural Networks (DNNs) are effective tools for learning complex patterns in such data; however, optimizing their architecture remains a...

Reasonably pricing data resources contributes to the development of the digital economy. However, in the early stage of the development of the data market, the data transaction volume is small, and the resulting small sample problem makes it difficult to accurately model and forecast the price...

Climate-driven market interdependencies critically influence emission reduction strategies, yet the causal mechanisms linking carbon, fossil, and clean energy markets remain poorly quantified -a knowledge gap impairing risk-aware policymaking and sustainable investments. This study uses the...