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Applied Data Mining for Forecasting Using SAS(R) by Tim Rey, Arthur Kordon, Chip Wells

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Chapter 4: Issues with Data Mining for Forecasting Application

4.1 Introduction

4.2 Technical Issues

4.2.1 Data Quality Issues

4.2.2 Data Mining Methods Limitations

4.2.3 Forecasting Methods Limitations

4.3 Nontechnical Issues

4.3.1 Managing Forecasting Expectations

4.3.2 Handling Politics of Forecasting

4.3.3 Avoiding Bad Practices

4.3.4 Forecasting Aphorisms

4.4 Checklist “Are We Ready?”

4.1 Introduction

One of the big differences between mechanistic or statistical models applied in manufacturing and forecasting models that are applied mostly in a business context is that the latter is a continuously changing environment and performance is judged according to users' sometimes unrealistic expectations. The motivation for using data mining ...

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