4 results about "Market prediction" patented technology

This invention belongs to the field of financial investment decision-making technology, specifically a financial investment decision-making assistance method and system based on game theory and multi-source big data. It collects and processes multi-source financial data, constructs a unified data foundation, and uses machine learning to generate multi-dimensional market prediction signals based on this foundation. These prediction signals are then input into a game theory model to simulate the behavior of market participants, solve for equilibrium strategies, and generate investment or risk control suggestions based on the game results, which are then visualized. This invention overcomes the limitations of single data sources by integrating multi-source big data and generating multimodal prediction signals. By combining game theory to model the complex interactive behaviors of market participants, it can deduce market evolution trends from a global perspective, thereby assisting in the formulation of more forward-looking investment or risk control strategies. It can simulate information asymmetry, strategy interaction, and dynamic evolution processes in the market, thus providing a better understanding of market fluctuations and the impact of sudden events.

This invention provides a method, system, device, and storage medium for power system risk control decision-making, belonging to the field of power system technology. The method includes: acquiring power system fusion data and power market text information, and labeling the text information with market sentiment tags; training an optimal electricity price sentiment mapping and an optimal sentiment mapping correction quantity based on the tags and text information, and extracting market sentiment features based on the optimal mapping and correction quantity; acquiring a market feedback time decay mapping based on the aforementioned text information, and fusing the text information, time decay mapping, and market sentiment features to obtain power market expectation features; then combining the power fusion data to predict the base predicted electricity price and the market predicted electricity price; and obtaining the base price tendency quantity under gating parameters based on the predicted electricity price to obtain the estimated electricity price; and generating a power anomaly alarm based on the estimated electricity price for risk control decision-making. This invention can improve the reliability of power system risk control decision-making.

The application discloses an enhanced model day-ahead electricity price prediction method based on dynamic feature selection, which extracts features from the original data set through a multi-head attention mechanism for multidimensional correlation analysis, generates a feature weight vector in real time to realize dynamic screening of the optimal feature subset, thereby suppressing redundant information and nonlinear noise features at the source; then, an enhanced XGBoost model is used to deeply mine the structured relationship of power market data, simultaneously combined with a Transformer model to capture the long-range time series dependence characteristics of the electricity price sequence, and through a gating mechanism to perform dynamic weighted fusion, to realize the complementary integration of spatial features and time series features; finally, a multi-objective Bayesian optimization algorithm is introduced to obtain a trained enhanced model through multi-objective collaborative optimization, and then prediction is performed. The application significantly improves the prediction accuracy and robustness, effectively solves the deployment problem of the model in a resource-limited environment, and has high engineering application value and power market prediction practicality.