8 results about "PROJECTIONS PREDICTIONS" patented technology

The application discloses a kind of day-ahead wind power power ramp event prediction methods, comprising the following steps: S1.day-ahead wind power prediction: using extreme value driving model, based on historical wind power and meteorological characteristics, produce the wind power prediction of Q time points of next day;S2.predicting confidence interval construction: using wind power oriented conformal inference method, for each future time point, construct confidence interval C;S3.ramp event detection based on confidence interval: the prediction result of next day wind power ramp event is obtained by confidence perception detection algorithm.The application improves the prediction accuracy and reliability of wind power ramp event with significant operation risk by fusing multi-scale time series feature analysis, customized loss function for extreme event and adaptive confidence interval construction technology with statistical guarantee.

This invention relates to the field of enterprise management technology, specifically to an intelligent prediction method and system for enterprise management indicators, comprising: a data processing module, a prediction model module, and a prediction analysis module; the data processing module processes structured and unstructured data; the prediction model module constructs a prediction model to predict business activities and financial statements; and the prediction analysis module performs sensitivity assessment on the prediction results. This system, through processing and analyzing structured and unstructured data, and by combining macro and micro perspectives, achieves the prediction of enterprise management indicators, thereby improving the universality of the prediction.

Embodiments of the present disclosure relate to performance monitoring on prediction model In an aspect, a terminal device receives, from a network device, an indication of a first input type, or an indication of a second input type or an indication associated with a switch between the first input type and the second input type for a prediction model. The terminal device takes one of the following as an input of the prediction model for a next prediction: prediction results of the prediction model, wherein the indication of the first input type is received or wherein the indication associated with the switch is received and a previous input of the prediction model is measurements; or measurements of the feature, wherein the indication of the second input type is received or wherein the indication associated with the switch is received and a previous input of the prediction model is prediction results.

The long-term and short-term intelligent prediction and early warning method for microseismic frequency and energy of rock burst mine comprises the following steps: 1. Importing microseismic event frequency and energy historical data; 2. Preprocessing the data of step 1; 3. Making a training set and a test set; 4. Building a microseismic prediction model; 5. Inputting the training set data into the prediction model for training; 6. Inputting the test set data into the prediction model for prediction, and recording the evaluation index result value; 7. Using the grid search method to determine the model hyperparameters; 8. Selecting the optimal hyperparameter combination of the evaluation index to the model of step 4; 9. Inputting the historical microseismic frequency and energy data into the model to realize short-term prediction; 10. Continuously adding the prediction results to the historical microseismic data to realize long-term prediction; and 11. Comparing the prediction value with the critical index to realize early warning. The present application can realize long-term and short-term intelligent prediction and early warning of microseismic frequency and energy through the above steps, has the advantages of intelligent prediction and early warning, solves the problems of poor generalization and poor dynamic prediction of traditional mathematical and physical models, and effectively improves the accuracy and timeliness of the intelligent prediction and early warning method for microseismic frequency and energy of rock burst mine.

The application belongs to the technical field of deep learning, and discloses a model migration-based airfoil gas-water dynamic coefficient prediction method.The prediction method comprises the following steps: establishing a sample database of airfoils and airfoil gas-water dynamic coefficients; generating an airfoil geometric image; generating an airfoil grayscale image; constructing a migration learning framework; and performing airfoil gas-water dynamic coefficient prediction.The prediction method solves the distribution deviation problem between air medium and water medium by constructing a model migration-based learning framework, efficiently realizes the cross-water-air medium domain migration of the model in a pre-training combined with fine-tuning manner, relaxes the dependence on airfoil hydrodynamic coefficient samples, and improves the efficiency and prediction accuracy of the prediction model.

In order to solve the problem that the existing time series prediction method cannot extract multi-scale information and the dependence between variables at the same time, a time series prediction method based on a multi-scale network is provided.The method first constructs a prediction model MSLA (Multi-scale and Local Attention), which uses a multi-scale network to extract information at different scales of data;secondly, the correlation information between variables is extracted through a local attention module;and then the weighted fusion is carried out through a feature fusion module;finally, the prediction is carried out.The prediction model achieves good results under different real scene data sets, and the model provided by the application can provide more accurate prediction results compared with other models through experimental verification, and good experimental results are achieved in the multi-scene experiment, which shows good generalization ability and has great application prospect in the civil aviation field.

The present application relates to a kind of structured data table field missing oriented prediction filling method, belong to data management, data preprocessing, machine learning and artificial intelligence technology cross field.The method of the present application includes the following steps: data reading and missing value detection, classification statistics;Complete record set DBSCAN clustering processing;Cluster training prediction model based on autoencoder;Missing value prediction;Predictive result fusion, determine final filling value;Filling value backfilling and verification.The method of the present application has advantages in filling precision, robustness, adaptability, engineering landing, etc., compared with prior art, can effectively solve the defects existing in prior art.

This invention relates to a method for predicting financing demand based on a heterogeneous clustering distributed lag model. The method includes the following steps: constructing a set of predictive and response variables for investors; establishing a heterogeneous clustering distributed lag model and training it using an improved K-means algorithm, determining the number of clusters using the Bayesian information criterion; and performing out-of-sample prediction based on the trained model to predict changes in the financing balance under different financing interest rate adjustment schemes. The method, apparatus, processor, and computer-readable storage medium for predicting financing demand based on a heterogeneous clustering distributed lag model of this invention aim to utilize economic and econometric models, combined with customer credit information and macroeconomic indicators, to predict the likelihood of customers adjusting financing interest rates and the changes in the financing balance after interest rate reductions. By identifying the customer group truly affected by interest rate changes, this helps companies adjust interest rate strategies more accurately and improve the efficiency of financing balance management.