157 results about "Predictor variable" patented technology

A system and method for predictive modeling of building energy consumption provides predicted building energy load values which are determined using kernel smoothing of historical building energy load values for a building using defined scaling factors for scaling predictor variables associated with building energy consumption. Predictor variables may include temperature, humidity, windspeed or direction, occupancy, time, day, date, and solar radiation. Scaling factor values may be defined by optimization training using historical building energy load values and measured predictor variable values for a building. Predicted and measured building energy load values are compared to determine if a preset difference threshold has been exceeded, in which case an alert signal or message is generated and transmitted to electronically and/or physically signal a user. The building energy monitoring system may be integrated with a building automation system, or may be operated as a separate system receiving building energy and predictor variable values.

The system and method of the present invention automatically assigns “scores” to the predictor/variable value pairs of a conventional probabilistic model to measure the relative impact or influence of particular elements of a set of topics, items, products, etc. in making specific predictions using the probabilistic model. In particular, these scores measure the relative impact, either positive or negative, that the value of each individual predictor variable has on the posterior distribution of the target topic, item, product, etc., for which a probability is being determined. These scores are useful for understanding why each prediction is made, and how much impact each predictor has on the prediction. Consequently, such scores are useful for explaining why a particular prediction or recommendation was made.

A method of obtaining and processing patient data and patient treatment data to provide a prognosis parameter related to a patient's disease state is provided. The method identifies and calculates coefficients related to appropriate predictor variables which are then used by the prediction model to calculate the prognosis parameter. The prediction model may be a logistic regression model. The method may also be used to assess the level of care being provided to patients, as well as providing a way of assessing the outcome of the patient's condition as a function of treatment. A method of calculating a harm index reflective of the risk of treatment is also provided.

A prediction processing system, method, and method for doing business is disclosed. The prediction processing system can collect, process and publish event-outcome information. The prediction processing system can dynamically filter participants into groupings and iteratively optimize odds calculations over time. A proposal framework consisting of a various abstract proposal types can model and settle propositions. Game propositions can be automatically generated based on event categorization relationships. The prediction processing system can provide game players or others with access to collective intelligence, including prediction information and information derived from prediction information. The prediction processing system can create groupings of better-performing predictors and provide them with additional information not generally available. Better-performing predictors can be provided with additional stakes to increase the weight of their predictions in odds calculations. The prediction processing system can provide access to event-outcome information on a for-fee subscription basis.

Embodiments of the invention utilize advanced statistical data analytics to predict and provide recommendations for root-cause analysis for service access QoE issues in networks, such as 3G/4G networks. Using FCAPS data as predictor variables, embodiments are configured to set up the problem as a predictive regression or classification problem to estimate service access QoE related indicators. Some embodiments perform training and tuning of various non-linear statistical modelling algorithms, based for example on tree and ensemble methods, using network deregistration information from RAN logs.

A method is disclosed for predicting the presence of haemostatic dysfunction. At least one time-dependent measurement on an unknown sample is performed and a respective property of the sample is measured over time so as to derive a time-dependent measurement profile. One or more predictor variables, including initial slope, are defined which sufficiently define the data of the time-dependent measurement profile. A model is then derived that represents the relationship between the abnormality and the set of predictor variables. Subsequently, the model is utilized to predict haemostatic dysfunction, such as septicemia or disseminated intravascular coagulation (DIC).

A system, method, and computer program product that uses attribute importance (AI) to reduce the time and computation resources required to build data mining models, and which provides a corresponding reduction in the cost of data mining. Attribute importance (AI) involves a process of choosing a subset of the original predictive attributes by eliminating redundant, irrelevant or uninformative ones and identifying those predictor attributes that may be most helpful in making predictions. A new algorithm Predictor Variance is proposed and a method of selecting predictive attributes for a data mining model comprises the steps of receiving a dataset having a plurality of predictor attributes, for each predictor attribute, determining a predictive quality of the predictor attribute, selecting at least one predictor attribute based on the determined predictive quality of the predictor attribute, and building a data mining model including only the selected at least one predictor attribute.

A tire wear estimation system is provided. The system includes at least one tire that supports a vehicle. At least one sensor is affixed to the tire to generate a first predictor. A lookup table or a database stores data for a second predictor. One of the predictors includes at least one vehicle effect. A model receives the predictors and generates an estimated wear rate for the at least one tire.

A multi-step iterative process for simulating complex agricultural situations where limited sets of data are available for such problems first predicts an outcome for each situation in a particular dataset, using initial assumptions of an applied primary model. The process then uses the errors across these situations to identify where opportunities exist among relevant predictive variables for the model to make changes to a response to such predictor variables to reduce the errors when averaged across all situations. The process then develops a correction model to identify adjustments based on combinations of the predictive variables, and applies the adjustments to the primary model to induce an altered outcome.

Exemplary video processing methods and apparatuses for coding a current block determine a number of OBMC blending lines for a boundary between a current block and a neighboring block according to motion information, a location of the current block, or a coding mode of the current block. OBMC is applied to the current block by blending an original predictor of the current block with an OBMC predictor for the number of OBMC blending lines. Some other exemplary video processing methods and apparatuses for coding a current block extend reference samples fetched from a buffer by a padding method to generate padded sample, and OBMC is applied to the current block or a neighboring block by blending an original predictor with an OBMC predictor generated from the extended reference samples.

An enhanced modeling tool associated with an entity may facilitate end-to-end modeling of problems in any application space. The enhanced modeling tool may collect modeling data from a variety of sources, check the collected data, find the best predictor variables for a given target variable, estimate the model, implement the model, and validate the model. The output of each of these steps may be in a standardized format to allow other steps to directly incorporate the output. An additional feature of the system may include a reporting capability that generates supporting documents related to model governance and risk compliance.

The invention discloses a maize yield combined prediction system and method. The prediction system comprises a data input module, a model base and intelligent combined module, a combined prediction module, a parameter management module and a result check and output module. The prediction method includes the steps that yield prediction is divided into sown area prediction and per-unit-yield prediction, models are selected to be combined according to predicted targets, the weights of sub-models of the combined model are determined according to the basic data change amplitude, the combined prediction result is acquired according to weight distribution of the combined model and checked, and finally the combined prediction result is output through diagrams and characters. Different prediction models are correspondingly combined according to different prediction durations, prediction variable factors and prediction basic data, maize yield prediction is pertinently conducted, the prediction result is higher in accuracy, the application range is wider, short-period and medium-and-long-term dynamic prediction can be achieved at the same time, and agricultural research requirements are met.

A computer-implemented method and system for building a neural network is disclosed. The neural network predicts at least one target based upon predictor variables defined in a state space. First, an input data set is retrieved that includes the predictor variables and at least one target associated with the predictor variables for each observation. In the state space, a number of points is inserted in the state space based upon the values of the predictor variables. The number of points is less than the number of observations. A statistical measure is determined that describes a relationship between the observations and the inserted points. Weights and activation functions of the neural network are determined using the statistical measure.

Method and apparatus for analyzing data to provide decision making information. In one embodiment, a method includes receiving data corresponding to an agent for one or more predictor variables of a model, and calculating coefficients of the model based, at least in part, on a logistic regression analysis for a response variable to determine probability densities of the response variable, wherein the response variable is associated with the one or more predictor variables. The method may further include performing a computational analysis of the response variable based on the probability densities of the response variable to determine variation in the probability densities of the response variable, and generating a decision matrix, reflecting probabilities of one or more response variables and analysis values.

A system and method for yield management are disclosed wherein a data set containing one or more prediction variable values and one or more response variable values is input into the system. The system can process the input data set to remove prediction variables with missing values and data sets with missing values based on a tiered splitting method to maximize usage of all valid data points. The processed data can then be used to generate a model that may be a decision tree. The system can accept user input to modify the generated model. Once the model is complete, one or more statistical analysis tools can be used to analyze the data and generate a list of the key yield factors for the particular data set.

Methods, systems, and apparatuses for generating relevance functions for ranking documents obtained in searches are provided. One or more features to be used as predictor variables in the construction of a relevance function are determined. The relevance function is parameterized by one or more coefficients. An ideal query error is defined that measures, for a given query, a difference between a ranking generated by the relevance function and a ranking based on a training set. According to a structured output learning framework, values for the coefficients of the relevance function are determined to substantially minimize an objective function that depends on a continuous upper bound of the defined ideal query error.

A method and apparatus for optimization of predictor variables. The apparatus includes a value obtaining section to obtain a plurality of answers from an answer database, each answer including an answered value for each variable of a plurality of variables, the plurality of variables answered by each respondent of a plurality of respondents, a cost obtaining section to obtain a cost for answering each of the plurality of variables from a cost database, and a determining section to determine at least one predictor variable from the plurality of variables based on a prediction performance and an answering cost, the at least one predictor variable for predicting at least one remaining variable of the plurality of variables.

A method, system, and computer program product for counting predictor-target pairs for a decision tree model provides the capability to generate count tables that is quicker and more efficient than previous techniques. A method of counting predictor-target pairs for a decision tree model, the decision tree model based on data stored in a database, the data comprising a plurality of rows of data, at least one predictor and at least one target, comprises generating a bitmap for each split node of data stored in a database system by intersecting a parent node bitmap and a bitmap of a predictor that satisfies a condition of the node, intersecting each split node bitmap with each predictor bitmap and with each target bitmap to form intersected bitmaps, and counting bits of each intersected bitmap to generate a count of predictor-target pairs.