Danna, one of our data scientists, explains when to use regression vs. time series, a subset of regression, for machine learning problems you want to solve.
A question you may have when it comes down to using time series and regression is which one should I use to solve my machine learning problem? One thing that may be confusing is that having a time feature does not necessarily mean you have a time series problem.
For example, you may have a data set of house prices with features describing the houses including the year that house was built. Even though you have a date as a feature, this is not a time series problem. In time series forecasting, we are generally interested in predicting something that is changing over time, but in this data set, we have several different houses with one date and will be predicting the prices of other houses. So, this is a regression problem.
For example, you may have a data set of house prices with features describing the houses including the year that house was built. Even though you have a date as a feature, this is not a time series problem.
Another thing that may tell you that your problem is regression and not time series is if there isn’t really a relationship with your target and time.
In time series problems, we expect observations close to each other in time to be more similar than observations far away, after accounting for seasonality. For example, the weather today is usually more similar to the weather tomorrow than the weather a month from now. So, predicting the weather based on past weather observations is a time series problem.