Has anyone here tried to predict a commodity's price by using other commodities prices as features in a machine learning algorithm? What techniques have been successful?
Based on your question there are couple of things which I would assume to answer your question:
- As you need to predict the commodity price the data which is collected is time series data.
- Since you want to use other commodity to predict, it means that you don't have any past data of the product which you want to predict.
The answer could be derived by performing some exploratory analysis on the existing data. i.e., based on your business understanding you need to decide which product is similar to the new product. This kind of techniques is used to understand the sale of the new product/how is it going to perform after the launch.
Techniques which can be used over here are Time Series Analysis like ARIMA, if seasonality is present then SARIMA, if no trend then Exponential Smoothing(too many spikes), there are other models like Auto Regression, Moving Average, Croston if there are 0's etc.
This is one way of looking at your problem.