233 results about "Price prediction" patented technology

A proactive adaptive radio methodology for the opportunistic allocation of radio spectrum is described. The methods can be used to allocate radio spectrum resources by employing machine learning to learn models, via accruing data over time, that have the ability to predict the context-sensitive durations of the availability of channels. The predictive models are combined with decision-theoretic cost-benefit analyses to minimize disruptions of service or quality that can be associated with reactive allocation policies. Rather than reacting to losses of channel, the proactive policies seek switches in advance of the loss of a channel. Beyond determining durations of availability for one or more frequency bands statistical machine learning also be employed to generate price predictions in order to facilitate a sale or rental of the available frequencies, and these predictions can be employed in the switching analyses The methods can be employed in non-cooperating distributed models of allocation, in centralized allocation approaches, and in hybrid spectrum allocation scenarios.

A system and method of aggregating and ranking stocks based on the earning capabilities of each stock. The novel system and method use a neighborhood model of pricing trend prediction to aggregate a plurality of “neighboring” or related stocks to predict pricing of one stock within the plurality of related stocks. The system facilitates investors trading stocks by using the novel methodology to rank the stocks and by having an easy-to-use interface.

A method of creating a price prediction model that forecasts short-term price fluctuations in financial instruments by collecting, analyzing and classifying financial news for a financial instrument into categories. Distributions for the changes in price of the financial instrument for a set period of time and distributions for the changes in price of the financial instrument as a result of the financial news for each news category for a set period of time are then obtained. If the distributions for the changes in price of the financial instrument are statistically significantly different than the distributions for the changes in price of the financial instrument for a particular news category, and the mean for the change in price is greater or less than zero, a signal is produced indicating the trading action that should be taken for the financial instrument.

An Internet based community predictor for equities and other assets collects and compiles member votes and makes predictions based on overall community statistics. Individual votes are cast by members representing price predictions for particular equities over a target period. By relying on aggregated data it is possible to make more accurate forecasts for the behavior of other populations as well.

Disclosed is a method based on a cyclic neural network of an attention mechanism for predicting a stock index price. The method comprises the following specific steps of 1) collecting and cleaning stock indexes and historical price data of a single stock, wherein the stock indexes include the historical price data; 2) using the attention mechanism to conduct weighting adjustment on model input values; 3) inputting the weighted model input values into the cyclic neural network for model training and prediction. By means of the method, the deep features of various influence factors of the stockindex price can be ingeniously extracted, and compared with a simple one-factor cyclic network or a traditional multi-factor cyclic network, the method can improve the accuracy of predicting the stockindex price.

A service level agreements supporting apparatus that makes it possible to adjust the service level and select a service satisfying the customer, for maintenance service of buildings and facilities involving work specifications. The apparatus has a service level setting unit for setting a service level, a service specification determination unit for determining work specifications of a service corresponding to the set service level, a service quality prediction unit for predicting a service quality based on the determined work specifications, a service price prediction unit for predicting a service price based on the determined work specifications, and a service content exhibition unit for exhibiting these kinds of information.

The invention belongs to the technical field of scheduling and repair of cascade hydropower stations and in particular relates to a method for double-layer optimization of medium- and long-term scheduling and repair plans of cascade hydropower stations in a market environment. According to the invention, in order to reduce the influence of repair plans of cascade hydropower stations on operation revenue, a cascade hydropower station medium- and long-term scheduling and repair plan double-layer optimization model is built to achieve joint optimization of medium- and long-term scheduling and repair plans. In a medium- and long-term scheduling optimization process, decisions are made according to runoff and price prediction and the genetic algorithm is employed to optimize the output of cascade hydropower stations in each period so as to search for the optimal revenue space. In a repair plan optimization process, with priority being given to the influence of the coupling relationships ofwaterpower and electric power of cascade upstream and downstream hydropower stations on repair plans and with an intermediate result of medium- and long-term scheduling being a boundary condition andthe minimum repair loss being an optimization target, the repair loss optimization result and the medium- and long-term power generation revenue are combined to be total revenue, so that joint optimization is achieved.

The invention discloses a deep network intelligent investment system data analysis method integrating an attention mechanism. The method includes the following steps that: step 1, financial fields called by a sufficient quantity of local devices are obtained from a financial website and a stock database, the financial fields are filtered and integrated, so that a field X can be obtained; step 2, the field X is inputted into an encoder module, wherein the encoder module is composed of a long-term and short-term memory network, and encodes the X; step 3, an encoded field X vector obtains an attention allocation probability distribution value within a probability distribution value interval through an attention allocation module; step 4, the long-term and short-term memory network in the decoder generates price predictions on the basis of a field code containing attention probability distribution and historical information that has been generated before; step 5, a trained deep network outputs the prediction result of a certain trading day, and compares the prediction result of the trading day with a set threshold value, and the risks of financial products are determined; and step 6, appropriate financial products are screened according to user funds, and an optimal investment portfolio is configured.

The invention provides a price parameter generation method and device of a power grid project equipment material. The price parameter generation method comprises the following steps: selecting a plurality of primary selection models from a preset model library based on a time dimension and a price volatility; selecting at least one final selection model from the plurality of primary selection models according to a plurality of preset indexes; and judging whether the number of the at least one final selection model is greater than 1, and if so, performing equal weight combination calculation by using the at least one final selection model to obtain comprehensive price parameters of the power grid project equipment material. According to the price parameter generation method and device of the power grid project equipment material provided by the invention, as a plurality of influence factors and a plurality of indexes of the price of the power grid project equipment material are comprehensively considered, compared with the traditional simple price statistical approach, the generated comprehensive price parameters are more accurate, and the price prediction risks of enterprises are reduced.

The invention discloses a carbon emission price combination prediction method. The method includes the steps that 1, an original carbon emission price sequence is decomposed into a series of intrinsic function components through a variation mode decomposition algorithm; 2, an output variable is given, the input variable of each IMF component is determined through statistical tools comprising a partial autocorrelation function and a corresponding partial autocorrelation diagram thereof; 3, each IMF component is predicted through a Spiking neural network; 4, prediction results of all the IMF components are superimposed to obtain a predicted value corresponding to an original carbon emission price. By means of the method, prediction precision is effectively improved, and carbon emission price prediction can be well achieved.

The present invention provides a commodity price prediction method and device, the method comprising: acquiring historical price information of commodities to be predicted corresponding to multiple first historical time points, historical weather index information corresponding to multiple second historical time points, and Forecast weather index information corresponding to multiple future time points; build a price prediction model based on historical price information and historical weather index information and train the price prediction model; treat the price of the forecast commodity based on the trained price prediction model and forecast weather index information Forecasting is performed to obtain forecasted commodity price values ​​corresponding to multiple future time points. In the embodiment of the present invention, by adding an anchor factor that can predict the trend in advance in the price prediction model, instead of relying on the explained variable itself to calculate the later prediction based on the previous prediction, the calculation error is reduced, thereby improving the price prediction model. Confidence in long-term forecasts.

PROBLEM TO BE SOLVED: To provide highly precise market price prediction data under the consideration of the congestion of a transmission line by using a statistical method from the past data concerning a support system for performing the transaction of power trade. ŽSOLUTION: This power market price prediction system predicts the congestion of a transmission line from the data of a demand prediction data storage device predicting the power demand of each bid area and the data of a transmission line data storage device connecting each of bid areas, and predicts a power market price by switching a model according to the result. Ž

The invention discloses a riding recommendation system for reducing online car-hailing expenses by dynamic price prediction, and provides journey expense prediction to recommend a riding scheme with few riding expenses by aiming at the online car-hailing riding requirements of a user at a certain time and place in a city. The method comprises the following steps that: the system subdivides the city into a plurality of areas and uses an entropy and a Fano inequation to calculate the dynamic price predictability of an area; then, the system selects a Markov chain predictor or a neural network predictor to carry out dynamic price prediction by aiming at areas of different predicable sizes; and finally, the system predicts the riding expenses and recommends a riding scheme with reduced expenses, for example, the user can obtain lower riding price if the user waits for a period of time in situ or moves for a distance. An experiment result indicates that the prediction result of the system is roughly matched with survey data, so that the user reduces anxiety generated due to uncertain riding expenses, and travel expenditures are saved.

The present invention provides a crude oil price prediction method and system based on the CEEMD-PSO-BP model and error compensation, comprising the following steps: using CEEMD to decompose the non-stationary crude oil price sequence into a series of IMF components and trend components with different frequency characteristics ;Use the PSO-BP model to predict each IMF component and trend component respectively, obtain the predicted value of each component and obtain the initial forecast sequence by linearly superimposing it; use the original crude oil price sequence to subtract the initial forecast sequence to obtain the error sequence; use CEEMD decomposes the error sequence into a series of imf components and trend components with different frequency characteristics, and uses the PSO-BP model to predict each imf component and trend component separately, obtains the predicted value of each component, and obtains the error prediction by linearly superimposing them sequence; finally, the initial forecast sequence and the error prediction sequence are linearly superimposed to obtain the final crude oil price prediction sequence. This method reduces the difficulty of crude oil price prediction and has higher accuracy.

The invention discloses a stock price prediction method based on a long-term and short-term memory neural network of an attention mechanism, and belongs to the field of deep learning and stock prediction. The method comprises the following steps of S1, obtaining stock historical data, performing data preprocessing on the stock historical data, and dividing the stock historical data into a trainingset and a test set; s2, performing data standardization on the training set and the test set, and performing wavelet transform processing on data of the training set to remove noise of the financialsequence; s3, initializing parameters required by the long-term and short-term memory neural network prediction model, constructing the long-term and short-term memory neural network prediction model,adding an attention mechanism layer into the long-term and short-term memory neural network prediction model, and training the long-term and short-term memory neural network prediction model by usingtraining set data; and S4, predicting the test set by using the trained prediction model to obtain a prediction result. According to the invention, the nonlinear change of the stock price can be better predicted.

The invention discloses a stock prediction method fusing a generative adversarial network and a two-dimensional attention mechanism. The method comprises the following steps: acquiring historical sequence data of a driving factor as stock sequence input; preprocessing the historical sequence data; divdiing the preprocessed historical sequence data into a training set and a test set, performing standardization, and generating two-dimensional data sequence input; weighting spatial attention of the two-dimensional data sequence input; weighting time attention pf the two-dimensional data sequence input after the space attention weighting; constructing a stock prediction preliminary model based on a two-dimensional time-space attention mechanism as a generator; modifying an output part structure of the generator to obtain a new generator; establishing a stock prediction model based on a newborn generator and a generative adversarial network discriminator; and constructing an optimization target of the stock prediction model to obtain an optimal stock prediction model. According to the invention, a more accurate and ideal stock price prediction result can be generated.