Compare Forecast Models to Other ML Models
Full prompt: What are the differences between forecast models compared to other ML models?
There are often features not available for forecast models that you would have in other ML models.
Seasonality and overall trends are often useful in forecasting models.
How do you determine how well a forecasting model is performing? How does this work in training vs. in production? How does this differ from traditional ML models and cross-validation?
How do you engineer useful features for forecasting models? How do you determine when your model needs updated? Does this differ from traditional models?
Forecasting offers a substantial difference from most other ML modeling techniques. In cases like recommendations or conversion rates, you have the features when you make your prediction. With forecasting, you don’t have the same sorts of features. Instead, you build out features related to overall trend and seasonality.
For example, let’s say you want to predict how many customers will arrive at your website every day in the next 6 months. You can use the day of the week (weekday vs. weekend, Monday vs. Tuesday vs. Wednesday), month of the year (how did traffic change last March, and how does this data influence this March?), or overall traffic over the past 6 months (has traffic been generally increasing?).
However, other relevant information about future dates remains unknown. Will the website be down on certain days? Will weather changes impact how customers act online? Adding estimates of these items to a model adds additional uncertainty that most models don’t have to consider.
What makes this answer effective
This answer shows familiarity with the unique complexity of forecasting models and offers solutions. It also shows knowledge of what features can and cannot be used.
Other considerations
With more time, consider discussing how to measure the model uncertainties and how the success metrics of different models might differ when deployed into production.