Abstract: In-database parallel analytics is disclosed. An example method includes receiving a function in a functional programming environment. The example method includes pushing operations for the function to a database layer for executing the operations. The example method also includes returning results of the operations to the functional programming environment.

Abstract: A report indicating a user-reported probability of a successful outcome is received. A behavior and information model is estimated based on the report. The behavior and information model includes a behavior model component having a bias parameter and a consistency parameter. The behavior and information model includes an information model component having a first user-believed probability of a successful outcome and a second user-believed probability of a successful outcome. The behavior and information model is used to yield a model-determined probability of a successful outcome that more accurately reflects a probability of a successful outcome than the user-reported probability of a successful outcome does.

Abstract: There is provided a computer-implemented method of generating a data forecasts for different levels of an entity. The method includes generating an aggregate forecast for an upper level entity comprised of two or more components. The method also includes determining mean values and a coefficient of variation for a probability distribution corresponding to future expected decomposition rates for each of the two or more components. A probability distribution parameter vector is computed based on the mean values and the coefficient of variation. The expected future decomposition rates for each of the two or more components may be computed based on the probability distribution parameter vector and a sample observation corresponding to previously observed decomposition values of each of the two or more components. Component forecasts corresponding to each of the two or more components may be computed based on the aggregate forecast and the expected future decomposition rates.

Abstract: Embodiments include methods, apparatus, and systems for forecasting using a time domain analysis. One embodiment is a computer implemented method that receives plural cycle lengths identified in time series data and builds a model using a time domain analysis of the time series data. The model is used to predict future events or future data points.

Abstract: In-database parallel analytics is disclosed. An example method includes receiving a function in a functional programming environment. The example method includes pushing operations for the function to a database layer for executing the operations. The example method also includes returning results of the operations to the functional programming environment.

Abstract: Drivers that affect or relate to revenue to be forecast are identified. Each driver is a variable. One or more particular drivers are selected from the drivers, based on an analysis of the lags between the revenue and the drivers as synchronized. A causal dynamic model for the revenue is constructed using the particular drivers selected.

Abstract: A crossover point between a first driver and a second driver over a series of time points is identified. Each of the first driver and the second driver is a variable, and affects or relates to revenue to be forecast. A composite driver from the first driver and the second driver is derived based on the revenue, using a model having one or more first weighting parameters for the time points before the crossover point and one or more second weighting parameters for the time points after the crossover point. The crossover point is a time point within the series of time points at which the revenue transitions from being more affected by the first driver than by the second driver to being more affected by the second driver than by the first driver.

Abstract: A method for determining call center resource allocation can include modeling call center performance over an operations time period using a computer. A number of replicas of the modeled call center performance are simulated, using the computer, over a planning time period, each replica having random contact arrivals and contact service times following a stochastic arrival and service process according to a probability distributions of inter-arrival time and service time. Multiple iterations of each simulation are run on the computer to optimize call center resource allocation. A particular simulation iteration is tested against a criterion of convergence, and call center resource is allocated based on the particular simulation iteration with a successful criterion of convergence.

Abstract: Embodiments of the present invention include a computational forecasting system that includes an identity of a dependent variable of interest and identities of a plurality of candidate indicators along with historical data or stored references to historical data, forecast-problem parameters stored in an electronic memory of the one or more electronic computers, an independent-variable selection component that generates correlations to the dependent variable of interest and lag times for the candidate indicators, and uses the generated correlations and lag times to select a number of the candidate indicators as a set of independent variables, and a model-generation component that, using a regression method, generates forecast models for the dependent variable of interest until a model that meets an acceptance criterion or criteria is obtained.

Abstract: Methods, machine readable media, and systems for market forecasting are provided. An example of a method for market forecasting includes modeling market characteristics of market participants for a type of product and deriving variability of an attribute corresponding to a market characteristic coefficient of the type of product for each of the market participants. The method includes resampling from a distribution of the variability of the attribute for each of the market participants and remodeling the market characteristics of the market participants for the type of product using the resampled attribute. The method includes forecasting future market characteristics of the market participants for the type of product according to the remodeled market characteristics.

Abstract: There is provided a system and method for estimating a parameter that represents data describing a physical system. An exemplary method comprises randomizing data representative of a population of items for which the parameter is known. The method may additionally comprise generating data representative of a pseudo population of items using a known perturbation, the data representative of the pseudo population of items being included with the data representative of the population of items for which the parameter is known to form a revised population and selecting a bootstrap sample of a minimum sample size of the revised population. A sensitivity study is performed on the parameters of the items comprising the bootstrap sample to determine a level of change of a predicted parameter value relative to a parameter value of the sample.

Abstract: A report indicating a user-reported probability of a successful outcome is received. A behavior and information model is estimated based on the report. The behavior and information model includes a behavior model component having a bias parameter and a consistency parameter. The behavior and information model includes an information model component having a first user-believed probability of a successful outcome and a second user-believed probability of a successful outcome. The behavior and information model is used to yield a model-determined probability of a successful outcome that more accurately reflects a probability of a successful outcome than the user-reported probability of a successful outcome does.

Abstract: Systems and methods for detecting an interesting event in a data stream. More specifically, a monitoring system is configured to monitor a data stream and establish a sensitivity parameter based on sequences generated from values in a first portion of the data stream. A detector may be trained using the sensitivity parameter to detect the occurrence of an interesting event in the data stream.

Abstract: One embodiment is a method that receives historical data of suppliers and applies, to the historical data, a mathematical optimization system that includes a set of mathematical equations and inequalities that express capabilities and capacities of the suppliers. The mathematical optimization system includes an objective function that minimizes a number of the suppliers to perform third-party labor services for an enterprise. The method selects a sub-set of the suppliers to perform the third-party labor services for the enterprise.

Abstract: Methods, machine readable media, and systems for market forecasting are provided. An example of a method for market forecasting includes modeling market characteristics of market participants for a type of product and deriving variability of an attribute corresponding to a market characteristic coefficient of the type of product for each of the market participants. The method includes resampling from a distribution of the variability of the attribute for each of the market participants and remodeling the market characteristics of the market participants for the type of product using the resampled attribute. The method includes forecasting future market characteristics of the market participants for the type of product according to the remodeled market characteristics.

Abstract: Disclosed herein are systems and methods for forecasting with model-based PDF (probability density function) estimates. Some method embodiments may comprise: estimating model parameters for a time series, calculating a PDF for the time series, and generating a forecast from the PDF. The model parameters may comprise a variance for a hidden noise source, and the PDF for the time series may be based at least in part on an estimated variance for the hidden noise source.

Abstract: A method for determining call center resource allocation can include modeling call center performance over an operations time period using a computer. A number of replicas of the modeled call center performance are simulated, using the computer, over a planning time period, each replica having random contact arrivals and contact service times following a stochastic arrival and service process according to a probability distributions of inter-arrival time and service time. Multiple iterations of each simulation are run on the computer to optimize call center resource allocation. A particular simulation iteration is tested against a criterion of convergence, and call center resource is allocated based on the particular simulation iteration with a successful criterion of convergence.

Abstract: A method of workforce plan evaluation includes receiving (110) a resource data (440) that includes data relating to employee resources, and data relating to a set of opportunities representing demand for employee resources; and receiving a workforce plan (450) associated with the resource data. A realization model configured to simulate effects of supply and demand uncertainty on the resource data is established (130) and executed (140) over a given time period (240) of the planning window (230) for which the workforce plan (450) is generated. The resource data (440) is transformed (150) according to the predictions of the resource model, and metrics configured to assess demand fulfillment and/or resource utilization achieved by the workforce plan under the realization model are computed (160).

Abstract: Embodiments include methods, apparatus, and systems for forecasting using a time domain analysis. One embodiment is a computer implemented method that receives plural cycle lengths identified in time series data and builds a model using a time domain analysis of the time series data. The model is used to predict future events or future data points.

Abstract: Embodiments of the present invention include a computational forecasting system that includes an identity of a dependent variable of interest and identities of a plurality of candidate indicators along with historical data or stored references to historical data, forecast-problem parameters stored in an electronic memory of the one or more electronic computers, an independent-variable selection component that generates correlations to the dependent variable of interest and lag times for the candidate indicators, and uses the generated correlations and lag times to select a number of the candidate indicators as a set of independent variables, and a model-generation component that, using a regression method, generates forecast models for the dependent variable of interest until a model that meets an acceptance criterion or criteria is obtained.