Abstract: A forecasting engine arranged to generate a forecast for a set of historic time-series data. The engine includes one or more one or more generically structured core algorithmic components providing a core function in a forecasting heuristic algorithm, and one or more generically structured optional algorithmic components providing an optional function in the forecasting heuristic algorithm. Each algorithmic component is individually tuned in a predetermined sequence, and the first algorithmic component in the sequence performs a tuning process on the set of historic time-series data. Subsequently, algorithmic components are tuned using time-series data conditioned by ail of the tuning processes previously performed in the predetermined sequence. The entire sequence of algorithmic components is arranged to collectively provide conditioned data which is used to generate a forecast.

Abstract: A forecasting system is regulated with time-series data. The context of the time-series data is determined by one or more parameters encapsulated within a forecast data type, the forecast data type being arranged to present the time-series data in a generic form (independent of any context information) to a forecasting algorithm of the forecasting system. The time-series data is encapsulated to enable the forecasting algorithm to generate a forecast for the time-series dependent on such context. The time-series data is retrieved using a generic forecast data type object arranged to provide the time-series in the predetermined context. The context presented by the fore-cast data type is capable of changing by the fore-cast data type representing a variable number and type of parameters to the forecasting system without requiring the forecasting system to be re-configured to provide the forecast over the time-series data.

Abstract: The present invention is a computer system that analyses the factual and behavioural data of a group of subjects, extracts the common behavioural patterns from the collection, and is capable of forecasting the behaviour of a new subject when his or her factual data are inputted to the system. It does so by first taking a collection of both the factual data and behavioural data from a group of subjects. A clustering engine is employed to compute a set of exemplars that concisely represent the population. Afterwards, the factual data of a subject, and the corresponding behavioural exemplar that he or she belongs, are fed to a learning module so that it can learn the mapping between the subject's factual data and behavioural exemplar. After learning, the system is able to predict the behaviour patterns when factual data of a new subject is presented.

Abstract: The present invention is a computer system that analyses the factual and behavioural data of a group of subjects, extracts the common behavioural patterns from the collection, and is capable of forecasting the behaviour of a new subject when his or her factual data are inputted to the system. It does so by first taking a collection of both the factual data and behavioural data from a group of subjects. A clustering engine is employed to compute a set of exemplars that concisely represent the population. Afterwards, the factual data of a subject, and the corresponding behavioural exemplar that he or she belongs, are fed to a learning module so that it can learn the mapping between the subject's factual data and behavioural exemplar. After learning, the system is able to predict the behaviour patterns when factual data of a new subject is presented.

Abstract: A forecasting engine arranged to generate a forecast for a set of historic time-series data, the engine comprising: one or more generically structured core algorithmic components providing a core function in a forecasting heuristic algorithm; and one or more generically structured optional algorithmic components providing an optional function in the forecasting heuristic algorithm, wherein each algorithmic component is individually tuned in a predetermined sequence, the first algorithmic component in said sequence performing a tuning process on said set of historic time-series data and subsequent algorithmic components are tuned using time-series data conditioned by ail of the tuning processes previously performed in said predetermined sequence, wherein the entire sequence of algorithmic components is arranged to collectively provide conditioned data which is used to generate a forecast.

Abstract: The present invention is concerned with a system for building at least one algorithm to apply to an optimisation problem in order to find a solution to the problem, where the or each algorithm comprises a plurality of components.