I have sale data for 3 years by week.I need to predict sales for the next year by week.

The business requested that some categorical values and numeric values (so for example category, product quantity offered and amount of weekly traffic) are included as coefficients that they can change for next year and accordingly the projection would change. This would be very easy with a multiple linear regression model. (I was planning on training the model on 2 years and testing it on the 3rd)

Since my data is definitely seasonal, I thought that I need to use a time series model. (I was thinking ARIMA, but any other recommendation is welcomed). However, in the past when I used ARIMA, I just had a timestamp and predicted the value at a future timestamp, without other coefficients that could be changed by the user.

It might be my misunderstanding of time series models, but is there a combination of a model that can have adjustable coefficients like a multilinear regression model yet does account for seasonality?

Or would extrapoliation be the way to go?

I looked at those two questions, but they don't help me directly.

[Linear regression vs Time series analysis](https://stats.stackexchange.com/questions/136629/linear-regression-vs-time-series-analysis)

[Time Series Forecasting vs Linear Regression Extrapolation](https://stats.stackexchange.com/questions/103209/time-series-forecasting-vs-linear-regression-extrapolation)