Publications

Edward R. Jones, DM Review Special Report, February 12, 2008

Traditionally analysts in retail, manufacturing and many other industries use a variety of statistical methods to solve a range of problems in forecasting, data classification and pattern recognition. Some of these methods include regression analysis, logistic regression, survival and reliability analysis and Auto-Regressive Integrated Moving Average (ARIMA) modeling. However, because each of these methods uses different software algorithms with different data assumptions, forecasters must learn to use an assortment of tools to solve problems and produce answers.

Fortunately, neural networks can replace all of these methods and produce forecasts as accurate as or better than those available from other statistical methods. In fact, neural networks offer many advantages, including: improved accuracy over traditional statistical methods; a unified approach to a wide variety of predictive analytics problems; and they requires fewer statistical assumptions and can manage complex predictive analytics tasks in a more automated way, which saves time for analysts and programmers. We�ll take a look at what neural networks are, and why they�re suited for certain kinds of analytics, particularly predictive analytics.

Edward R. Jones is the senior statistical advisor for Visual Numerics, Inc. in the IMSL Product Development department.

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