1. A computer-implemented method for demand modeling and prediction for a set of products in a retail category, the method comprising: obtaining aggregated sales data in a retail category, said aggregated sales data comprising a combination of: a first sales data set having a time series of unit-prices and unit sales for a set of designated products in a product set from said retail category, said time series data being provided over a sequence of sales reporting periods from one or more retail stores; and, a second sales data set including data associated with attributes of a product in said first sales data set; generating a model for price movements and price dynamics from said time series data of unit-prices in the aggregated sales data; generating a model for market share of each said product in the retail category using the aggregated sales data and said integrated additional product attribute data; generating a model for an overall market demand in the retail category from the aggregated sales data; and, obtaining a predicted demand for one or more said products in the designated product set in retail category by combining the models for said price movements and price dynamics, said market share, and said overall market demand, wherein a programmed processor device performs said obtaining, said generating a price movement model, said generating a market share model, said generated overall market demand and said combining.