Commodity Prices Prediction

Part of my master project, which includes the following algorithms

• ARIMA
• Gaussian Process
• Multi-Task Gaussian Process
• Multi-Task Index Gaussian Process (Use index of each task)
• Deep Gaussian Process with Multi-Task Output
• Deep Sigma Point Process with Multi-Task Output
• Sparse Multi-Task Index Gaussian Process
• Sparse Matern Graph Gaussian Process
• Deep Graph Kernel
• Deep Graph Kernel + Deep Graph Infomax Pretraining
• Cluster Multi-Task GP (Pyro + Gpytorch)
• Non-Linear Deep Multi-Task GP
• Non-Linear Deep Sigma Point Process
• Cluster Non-Linear Deep Multi-Task GP
• Cluster Non-Linear Deep Sigma Point Process
• Learning Graph GP
• Graph Propagation Deep GP
• Interaction Net Deep GP
• DSPP Graph Propagation GP
• Interaction Net DSPP
• Non-Linear Deep Multi-Task GP Multi-Output
• Non-Linear Deep Sigma Point Process Multi-Output

See main.py for examples. Running a Test for Data-Splitting Algorithm. The data should be stored in data/{metal_name}.

In order to run the experiments, we assume to have a raw_data folder that contains folders that named after the commodities, which have {commodity name}_feature.csv and {commodity name}_raw_prices.csv (this should be raw prices not log of it) storted within. To create a preprocessed data that is saved in folder data, we run save_date_common("raw_data", "data") from utils.data_preprocessing.

We can run the test by:

python -m pytest

For example, we have:

One may be interested in training the GP within google colabs, we have provided a simple way to zip the necessary files/folder

sh upload/zip_folders.sh

where we can upload to the colabs, extract the file and then perform the training.