Why the Attraction for the Offensive Paradigm? In addition to the reasons provided by Green and Armstrong, I'd like to add one more reason for the lure of complexity: You can always add complexity to a model to better fit the history. In fact, you can always create a model
"The Role of Model Interpretability in Data Science" is a recent post on Medium.com by Carl Anderson, Director of Data Science at the fashion eyeware company Warby Parker. Anderson argues that data scientists should be willing to make small sacrifices in model quality in order to deliver a model that
There are some things every company should know about the nature of its business. Yet many organizations don't know these fundamentals -- either because they are short on resources, or their resources don't have the analytical skills to do the work. The summer research projects offered by the Lancaster Centre for Forecasting,
And now for the five steps: 1. Ignore industry benchmarks, past performance, arbitrary objectives, and what management "needs" your accuracy to be. Published benchmarks of industry forecasting performance are not relevant. See this prior post The perils of forecasting benchmarks for explanation. Previous forecasting performance may be interesting to know, but
Last time we saw two situations where you wouldn't bother trying to improve your forecast: When forecast accuracy is "good enough" and is not constraining organizational performance. When the costs and consequences of a less-than-perfect forecast are low. (Another situation was brought to my attention by Sean Schubert of
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