Kress, G. (1985). Practical techniques of business forecasting. Westport, CT: Quorum Books.Kress, G. (1985). "Practical techniques of business forecasting: fundamentals and applications for marketing, production, and financial managers"; Westport Conn.; Quorum Books....
Forecasting is important to nearly every business, but it can be hard to get right. As an MBA and a management consultant with over 30 years of business experience, I've seen the forecasting challenges of countless executives and managers. I've also faced some of these same issues myself ...
Describes how diffusion models can be used to forecast the sales of a new product with no or some... J Morrison - 《Journal of Business Forecasting Methods & Systems》 被引量: 0发表: 1996年 Forecasting and Operational Research:A Review From its foundation, operational research (OR) has made...
theories forecasters subscribe to and find that they are pronounced conservative in the sense, that they overwhelmingly rely on methods and theories that have been well-established for a long time, while more recent approaches are relatively unimportant for the practice of business cycle forecasting....
There are four main types of forecasting methods thatfinancial analystsuse to predict futurerevenues, expenses, and capital costs for a business. While there is a wide range of frequently used quantitative budget forecasting tools, in this article, we focus on four main methods: (1) straight-lin...
There are four main types of forecasting methods thatfinancial analystsuse to predict futurerevenues, expenses, and capital costs for a business. While there is a wide range of frequently used quantitative budget forecasting tools, in this article, we focus on four main methods: (1) straight-lin...
Forecasting Demand: Quantitative and Intuitive Techniques. Brian H. Archer. International Journal of Tourism Management, vol. 1, no. 1, March 1980, pp. 5-12. IPC Business Press Ltd., Oakfield House, Perrymount Road, Haywards Heath RH16 3... Archer, B.H., 1980, "Forecasting Demand: Quan...
imputation; providing an analysis for possibilities of missing values imputation with decision trees, EM algorithm and regression models; development of multistep forecasting functions on the basis of autoregression models; illustration of application of some selected perspective methods for missing data ...
Traditional forecasting methods used data from surveys and consumer insights combined with expert judgment and mathematical formulas to improve the accuracy of business predictions. However, traditional methods relied on historical data and often missed sudden market shifts. Plus, collecting consumer insights...
Specifically in the case of fashion forecasting since each product is associated with several factors, e.g. price, style, color and even human factors, learn a suitable predictive model is not an easy task. In fact, the challenge here boils down to learn a powerful model, which can cover ...