Abstract: Systems and methods for modeling and forecasting cyclical demand systems in the presence of dynamic control or dynamic incentives. A method for modeling a cyclical demand system comprises obtaining historical data on one or more demand measurements over a plurality of demand cycles, obtaining historical data on incentive signals over the plurality of demand cycles, constructing a model using the obtained historical data on the one or more demand measurements and the incentive signals, wherein constructing the model comprises specifying a state-space model, specifying variance parameters in the model, and estimating unknown variance parameters.

Abstract: A method for providing a forecast includes providing a multi-dimensional database storing data at a lowest level in a first dimension, calculating a first forecast at a level that is higher than the lowest level of a first dimension in the database, calculating a forecast for each category within the lowest level of the first dimension, aggregating a second forecast across a category at the lowest level of the first dimension based upon an aggregation of the calculated forecast for the category within the lowest level of the first dimension, determining a difference between the first forecast and the second forecast, creating a dummy category including a new category at the lowest level of the first dimension, and storing the difference in the dummy category at the lowest level of the first dimension.

Abstract: The computer creates a utility demand forecast model for weather parameters by receiving a plurality of utility parameter values, wherein each received utility parameter value corresponds to a weather parameter value. Determining that a range of weather parameter values lacks a sufficient amount of corresponding received utility parameter values. Determining one or more utility parameter values that corresponds to the range of weather parameter values. Creating a model which correlates the received and the determined utility parameter values with the corresponding weather parameters values.

Abstract: A method (and system) for providing a forecast, the method including providing a multi-dimensional database storing data at a lowest level in a first dimension, calculating a first forecast at a level that is higher than the lowest level of a first dimension in the database, calculating a forecast for each category within the lowest level of the first dimension, aggregating a second forecast across all categories at the lowest level of the first dimension based upon an aggregation of the calculated forecasts for each category within the lowest level of the first dimension, determining a difference between the first forecast and the second forecast, and storing the difference in a dummy category at the lowest level of the first dimension.

Abstract: The computer creates a utility demand forecast model for weather parameters by receiving a plurality of utility parameter values, wherein each received utility parameter value corresponds to a weather parameter value. Determining that a range of weather parameter values lacks a sufficient amount of corresponding received utility parameter values. Determining one or more utility parameter values that corresponds to the range of weather parameter values. Creating a model which correlates the received and the determined utility parameter values with the corresponding weather parameters values.

Abstract: A system for predicting a failure of equipment from prior maintenance data of the equipment collected during a time duration estimate a number of preceding failures of the equipment prior to the time duration. The system constructs a model, based on the prior maintenance data, of an impact of an external intervention on a failure of the equipment. The system constructs a model, based on the constructed model of the impact of the external intervention and the estimated number of preceding failures, of a replacement policy of the equipment and a probability of a subsequent failure of the equipment in a subsequent time period.

Abstract: A system, method and computer program product for predicting a failure of equipment from prior maintenance data of the equipment collected during a time duration estimate a number of preceding failures of the equipment prior to the time duration. The system, method and computer program product construct a model, based on the prior maintenance data, of an impact of an external intervention on a failure of the equipment. The system, method and computer program product construct a model, based on the constructed model of the impact of the external intervention and the estimated number of preceding failures, a replacement policy of the equipment and a probability of a subsequent failure of the equipment in a subsequent time period.

Abstract: A system, method and computer program product for predicting a failure of equipment from prior maintenance data of the equipment collected during a time duration estimate a number of preceding failures of the equipment prior to the time duration. The system, method and computer program product construct a model, based on the prior maintenance data, of an impact of an external intervention on a failure of the equipment. The system, method and computer program product construct a model, based on the constructed model of the impact of the external intervention and the estimated number of preceding failures, a replacement policy of the equipment and a probability of a subsequent failure of the equipment in a subsequent time period.

Abstract: A system for predicting a failure of equipment from prior maintenance data of the equipment collected during a time duration estimate a number of preceding failures of the equipment prior to the time duration. The system constructs a model, based on the prior maintenance data, of an impact of an external intervention on a failure of the equipment. The system constructs a model, based on the constructed model of the impact of the external intervention and the estimated number of preceding failures, of a replacement policy of the equipment and a probability of a subsequent failure of the equipment in a subsequent time period.

Abstract: A method (and system) for providing a forecast, the method including providing a multi-dimensional database storing data at a lowest level in a first dimension, calculating a first forecast at a level that is higher than the lowest level of a first dimension in the database, calculating a forecast for each category within the lowest level of the first dimension, aggregating a second forecast across all categories at the lowest level of the first dimension based upon an aggregation of the calculated forecasts for each category within the lowest level of the first dimension, determining a difference between the first forecast and the second forecast, and storing the difference in a dummy category at the lowest level of the first dimension.

Abstract: An embodiment of the present invention proposes to describe an enterprise or company in terms of its structure and represent that structure in performing revenue forecasts for the enterprise or company. Mapping the company structure in a multi-dimensional matrix, for example, can represent that structure. The revenue forecasting method is novel in that forecasts for any level of the enterprise or company make use of data and previous forecasts for that and other elements of the structure. In this way, the method improves upon existing methods by leveraging information contained in some data on other data, and learning the relations between them.

Abstract: A fast way for determining the best subset test for a nominal attribute in a decision tree. When a nominal attribute has n distinct values, the prior art requires computing the impurity functions on each of the 2.sup.n-1 -1 possible subset partitioning of the n values and finding the minimum case among them. This invention guarantees the minimum impurity test on the attribute by computing only (n-1) impurity function computations. This reduction of computational complexity makes it practically possible to find the true best tests for many real data mining application, where a binary decision tree is used as the classification model.