Abstract: A computer method, apparatus and storage medium is provided for creating quantitative aesthetic graphics from data. The invention utilizes a graph algebra to construct graphs and visually or otherwise represents the graphs as a quantitative aesthetic graphic representation. To create the quantitative aesthetic graphics from data, the data is indexed to form a data set. Thereafter, the data is converted into a variable data structure composed of an index set, a range and a function. The variable data structure is converted into a variable set by using at least one of a blend step, a cross step and a nest step. The variable set is mapped into a set of points and the set of points is mapped into an aesthetic representation.

Abstract: A computer method, apparatus and storage medium is provided for creating quantitative aesthetic graphics from data. The invention utilizes a graph algebra to construct graphs and visually or otherwise represents the graphs as a quantitative aesthetic graphic representation. To create the quantitative aesthetic graphics from data, the data is indexed to form a data set. Thereafter, the data is converted into a variable data structure composed of an index set, a range and a function. The variable data structure is converted into a variable set by using at least one of a blend step, a cross step and a nest step. The variable set is mapped into a set of points and the set of points is mapped into an aesthetic representation.

Abstract: A method and computer system is provided for automatically constructing a time series model for the time series to be forecasted. The constructed model can be either a univariate ARIMA model or a multivariate ARIMA model, depending upon whether predictors, interventions or events are inputted in the system along with the series to be forecasted. The method of constructing a univariate ARIMA model comprises the steps of imputing missing values of the time series inputted; finding the proper transformation for positive time series; determining differencing orders; determining non-seasonal AR and MA orders by pattern detection; building an initial model; estimating and modifying the model iteratively.