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Combine machine learning feature selection with time series

I have basic knowledge in time series prediction and supervised/unsupervised machine learning algorithms(clustering,classification,decision tree,etc.) I am now given a task to predict a bunch of stock price. Each stock has its previous trading price (a period of 18 months) as well as some other features: coupon, asset rating, industry,etc. I only now how to use time series analysis or supervised machine learning separately, I have no idea of how to combine these two together.Is there any particular algorithm that I can use as a predictive model? What are steps to combine both dynamic and static information? Any help will be appreciated!