Temporal Aspects in Machine Learning Concept drift means that the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways.

With reference to the classic house price prediction use case:

House prices change over time thus the model I use today could make no sense in the future.

What is the best approach to address concept drift?


Do we keep updating the input replacing older house prices of yesteryear?
Do we add an extra feature for Date of Sale - by including a temporal aspect as a feature with larger data sets? 
Do we eventually change model hyperparameters during training to build a model that fits better the new data?
It's not really possible to adress concept drift in general. But I can bring two similar answers for drift of houses prices :

As other prices the drift is usually well measured and studied. As one would correct price for inflation, one can correct past house prices with a housing index (typically this index for the US). It will help your model having prices that are comparable over years.

Another way to tackle drift is to consider a ratio with a relevant variable that has a similar drift. For housing price, that might be median income of the neighborhood. This will give you a variable that is less sensitive to the overall drift.


As you can see those two methods are pretty much equivalent here in practice, as it mainly consist in correcting features and eventually, targets. The main difference is that in the first case you talk about dollars directly which is often more business oriented. Application of those methods can get a bit difficult if you try to use your model to predict the future and need to project housing index or median wage.