0
$\begingroup$

I have already implemented a univariate time series forecasting using ARIMA in statsmodel. I know that in R we can put external reggressors using auto ARIMA. Can this be done with statsmodel's ARIMA implementation?

ex: a store has different features like sales, dayOfWeek, promotion and schoolHoliday. I want to use all these features of day d-1 to predict sales of d. Can this be done using the ARIMA implementation of the stats model?

$\endgroup$
  • $\begingroup$ So "multivariate" in the sense of multiple independent variables, not multiple dependent variables? When I see "multivariate" in the title, it is the latter that comes to mind first. $\endgroup$ – Richard Hardy Nov 29 '18 at 7:14
  • $\begingroup$ I edited the question with an example as to what I am actually looking for. $\endgroup$ – Suleka_28 Nov 29 '18 at 7:50
0
$\begingroup$

It is prudent to test for contemporaneous and lag effects for some user specified variables. Often there can be a lead effect where sales are affected the day before a price change at the next period. Care should be taken to detect and incorporate latent deterministic structure ala http://docplayer.net/12080848-Outliers-level-shifts-and-variance-changes-in-time-series.html as they have the potential to unmask effects that might otherwise be discarded as non-significant.

Identifying the form of the response between a dependent series and stochastic inputs is best handled by following http://www.math.cts.nthu.edu.tw/download.php?filename=569_fe0ff1a2.pdf&dir=publish&title=Ruey+S.+Tsay-Lec1 and https://onlinecourses.science.psu.edu/stat510/node/75

I don't believe these suggested identification approaches are part of the fitting software that you are considering and you might be better served by alternatives.

$\endgroup$

Your Answer

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

Not the answer you're looking for? Browse other questions tagged or ask your own question.