This is a regression problem that involves predicting the price of e.g. aluminum, oil, strawberries. I have hourly and half hourly data for the weather and up to 10 different socioeconomic variables (all numeric), for roughly 8 years.
I want to make a machine learning model that extrapolates the price 7 days into the future. Would it be more suitable to use gradient boosted decision trees or neural networks? I'm leaning towards using trees because a shallow leaning method might be better for this problem.