Forecast models are always wrong. The question is by how much? Explore the reasons for this and other kinds of forecast models. Defending why iterating on forecasts and modeling things more than once is critical. Learn about risks in forecasting and be aware of other forecast models. It's important to iterate on models and retest.
- One thing is true of all forecast models: they are always wrong. The question is, by how much? Some models fall well within acceptable margins of error or their predictions fall within confidence intervals. But other times, that doesn't happen, and the predictions end up wildly different from the actual data. There are four key tips to making good statistical forecast models. First, test and retest models to ensure that the models are still providing valid forecasts, even as data sets expand and new data is released. Second, measure model errors to improve your analysis and forecasts. If you don't measure errors, you can't improve upon them. Third, add more good data. If a model isn't performing well, you probably need more data and not just more data, but more good data when you're doing your analysis or forecasting. A good example of this would be looking if you can add more historical data. The longer the amount of time the data you have feeds into the model, the more predictive it will be under various circumstances in the future. Fourth, iterate on analytical models to constantly improve the potential to derive implications. You don't just build models and then leave them. You have to make sure you're continually revisiting the assumptions and the data to make sure you're producing the most accurate forecast possible. By following these four tips, you can improve the validity of your forecast models because even if forecasts are always wrong, there's still the opportunity to make them wrong as little as possible.