Analytics and Technology for Practical Forecasting

Analytics and Technology for Practical Forecasting

William Fox (College of William and Mary, USA) and Anh Ninh (College of William and Mary, USA)
Copyright: © 2020 |Pages: 35
DOI: 10.4018/978-1-7998-0106-1.ch006

Abstract

With the importance of forecasting in businesses, a wide variety of methods and tools has been developed over the years to automate the process of forecasting. However, an unintended consequence of this tremendous advancement is that forecasting has become more and more like a black box function. Thus, a primary goal of this chapter is to provide a clear understanding of forecasting in any application contexts with a systematic procedure for practical forecasting through step-by-step examples. Several methods are presented, and the authors compare results to what were the typical forecasting methods including regression and time series in different software technologies. Three case studies are presented: simple supply forecasting, homicide forecasting, and demand forecasting for sales from Walmart.
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Introduction To Forecasting

What is forecasting? We assume that forecasting is concerned with making predictions about future observations based upon past data. Mathematically, there are many algorithms that one might use to make these predictions: regression (linear and nonlinear), exponential smoothing (time series) model, autoregressive integrated moving average (ARIMA) model to name a few that we will illustrate in this chapter.

Applications of forecasting might include the following:

  • Operations management in business such as forecast of product sales or demand for services

  • Marketing: forecast of sales response to advertisement procedures, new item promotions etc.

  • Finance & Risk management: forecast returns from investments or retirement accounts

  • Economics: forecast of major economic variables, e.g. GDP, population growth, unemployment rates, inflation; monetary & fiscal policy; budgeting plans & decisions

  • Industrial Process Control: forecasts of the quality characteristics of a production process

  • Demography: forecast of population; of demographic events (deaths, births, migration); forecasting languages demand, forecasting crime or homicides

To facilitate the process of deciding which forecasting process to use, we employ the following mathematical modeling approach within the decision process.

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