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:
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:
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:
A trend of attributes associated with plural market participants is determined. A representation of the trend is computed, and market models for the market participants are built according to the representation of the trend.
Abstract:
A time series of data values representing occurrences of events at plural time points is received. Durations between successive events are computed, and a burst of activity based on the computed durations is detected. It is determined that a change has occurred in response to detecting the burst of activity.
Abstract:
To forecast data, an initial collection of data having a first length is received. In response to determining that the first length of the initial collection of data is insufficient for performing forecasting using a forecasting algorithm, an order of the initial collection of data is reversed to provide a reversed collection of data. Forecasting is applied on the reversed collection of data to estimate additional data values to combine with the initial collection of data to provide a second collection of data having a second length greater than the first length. The forecasting algorithm is applied on the second collection of data.
Abstract:
To perform data quality assurance, data values from a data source at discrete time points up to time point t are received. At least one estimated value is computed based on at least some of the received data values, and the received data values and estimated data values are applied to an algorithm. A data quality determination of the data value for time point t is performed based on the algorithm.
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. At least one of a range, a probability distribution or the minimum sample size is revised based on the parameter for items comprising the bootstrap sample to produce at least one of a revised range, a revised probability distribution or a revised minimum sample size, taking into account an effect of the known perturbation applied to the pseudo population. The steps of selecting, performing and revising are repeated until the sensitivity study indicates that the level of change of the parameter is acceptably small. A value of the parameter is estimated for the population based on a parameter corresponding to the acceptably small level of change.
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.