I have a dataset of X1,X2,X3,etc. to predict the number of units, and one or some of my X variables are lagged versions of the units (my Y variable) I am trying to predict in addition to other explanatory variables. I am planning on using ARIMAX, linear regression, XG Boost, and Random Forest.

Let's say I have the following data set

Week Y   X1 X2 X3 
1    90  90 67 67
2    98  89 88 34
3    56  89 67 67
4    78  90 68 67

In order to forecast week 5 and so on, how does this work with the models I plan to use? To use linear regression as an example, the model will be trained/tested on my current data set up to week four and generate the coefficients against the X inputs. In order to predict week 5-20, do I need to forecast out separately the trends of X1, X2, X3 as univariate time series?

Essentially, I am trying to understand what happens mathematically for the X1, X2, X3 for weeks 5-20 such that the model is able to generate the Y for these weeks.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.