Unidirectional and bidirectional LSTM models for edge weight predictions in dynamic cross-market equity networks

DeepResTrade: a peer-to-peer LSTM-decision tree-based price prediction and blockchain-enhanced trading system for renewable energy decentralized markets

“…A hierarchical electricity trading scheme is presented to optimize power system functioning in smart grids using peer-to-peer electricity trading (Symiakakis and Kanellos, 2023;Niaei et al, 2022) presented an energy-sharing architecture based on peer-to-peer (P2P) that maximizes customer involvement, cost, revenue, and real-time programming to maximize profitability and revenue while managing unpredictability. Results for edge weight prediction in financial networks are obtained through the construction and training of deep learning models, with implications for portfolio diversification and systemic risk management (Bhattacharjee et al, 2022;Jin et al, 2023) presented a peer-to-peer (P2P) trading technique for power management in nanogrids using renewable energy sources (RES) and energy storage systems (ESSs). Electricity costs in smart homes are reduced by optimizing smart appliance scheduling and using a smart bidding technique for P2P trade, which minimizes grid reliance and leads to significant cost reductions (Kanakadhurga and Prabaharan, 2022;Timilsina and Silvestri, 2023) describes an automated framework for peer-to-peer energy trading that takes into consideration user perception and behavioral modeling to maximize utility for buyers while increasing profitability for sellers.…”

Financial Time Series Modelling Using Fractal Interpolation Functions

“…These models take into account the autoregressive properties of such data and are thus commonly used in the existing literature to model returns/growth rates (ARIMA) or volatility (GARCH). In the last few years, there is an emerging trend using machine learning or deep learning approaches, such as Support Vector Machines (SVM), Long Short-Term Memory (LSTM) or other neural networks ( [13][14][15][16]). Such approaches can provide greater predicting accuracy but are often prone to overfitting, thus resulting in data-specific findings ( [17]).…”