# 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?
• What you are talking about is no longer cross-section regression that looks at features that determine prices at single point in time. Obviously you can build time series model that will include e.g. lag_house_price. However, to measure impact of different features on house prices over time you would have to build panel data model. You can have a look at some introductory econometric analysis resources. Btw. it is more cross-validated question. Apr 17 '18 at 15:50
• Appreciated, but in all honesty if this is not mentioned in such a course, then this is remis. I recognize time series from stock markets and such, but did not quite equate the two. What I think you are saying is that, at the very least, we should refresh datasets. On a final note, not sure why it would not be part of Data Science. Thx. Apr 17 '18 at 17:34
• I do not think it is time series in any event. Apr 17 '18 at 23:23
• Ok, I if you talk about It strikes me that house prices changes over time. or replacing older house prices of yesteryear clearly specifies that you want to add extra dimension to the problem. However, your question is horribly unclear since you do not provide model you are talking about. Therefore, are you trying to predict house price $Y = X \beta + \varepsilon$ or your variables have not only $i = 1, ..., n$ dimension but also $t$ and therefore you aim to build panel data model. It seems to me you cannot distinguish between cross-section and panel problem. Apr 18 '18 at 7:44
• Don't I state that I am talking about Linear Regression? Which may have more than one feature. I think it is a valid question, but thanks anyway. It's interesting that you mentioned time series. I thought about it, we don't know when next a house will be sold. Apr 18 '18 at 8:17