Predicting next element of a sequence given small amount of data I have data of bank branches and amount of revenue they have generated in a month. The data looks like this:

I am tasked to find the expected revenue for the branch for the next month using machine learning. Initially I was planning to use LSTM networks for such analysis, but I doubt its possible with such small amount of data.
I personally think machine learning is an overkill for such task. What would be the most appropriate way to predict the revenue for next month? I thought about increasing the amount of data by treating every branch as equal and using the row corresponding to each branch as separate instance for training (but I doubt that is a correct approach).
Any advice would be appreciated
You might find the link helpful.
https://towardsdatascience.com/how-to-model-time-series-data-with-linear-regression-cd94d1d901c0