Stock price prediction is crucial but also challenging in any trading system in stock markets.
Currently, family of recurrent neural networks (RNNs) have been widely used for stock …

This study aims to estimate the prices in the next 24 h with deep learning methods in the
Turkish electricity market. The model is based on hourly data for the period 2017–2021 …

Quantitative stock selection is one of the most challenging FinTech tasks due to the non-
stationary dynamics and complex market dependencies. Existing studies rely on channel …

Leaf diseases may harm plants in different ways, often causing reduced productivity and, at
times, lethal consequences. Detecting such diseases in a timely manner can help plant …

This paper proposes a trend prediction analysis method based on the evolution of
knowledge graph fusion for the analysis of price fluctuation trends with limited information …

In the distributed systems landscape, Blockchain has catalyzed the rise of cryptocurrencies,
merging enhanced security and decentralization with significant investment opportunities …

Utilizing market forecasts is pivotal in optimizing portfolio selection strategies. We introduce
DeepClair, a novel framework for portfolio selection. DeepClair leverages a transformer …

Recently, there has been a huge increase in online sales. Therefore, increasing e-
commerce sales improves the quality of business sales. To increase the efficiency of this …

Predicting future stock volatility has always been a demanding chore for research studies.
Individuals around the world have long regarded the stock market as a substantial profit. A …

Predicting financial stock prices, which are complex, volatile, and nonlinear, poses a
significant challenge due to the multitude of influencing factors and inherent uncertainty in …