Introduced by Moataz Rashad, Founder & CEO of DeepVu, http://deepvu.co, @deepvuhq, @moatazr.
DeepVu is a deep-learning startup focused on optimizing supply chains for manufacturers. We work with tier-1 manufactures in the US and Asia.
One of the secondary sub-use-cases that we encounter involves forecasting the price of certain commodities that are key constituents of our customer’s products Bill of Materials. If it is often needed to forecast the price of that commodity (for example, copper, PVC, aluminum, IronOre etc) several months into the future and in some cases a year out in order to inform our model’s predictions of the price of the manufactured parts.
For this competition project, you’ll be given a commodity’s market data, typically 5 years, and you’ll have the following attributes (open-price, close-price, trading volume, day high, day low, etc.)
You do have the freedom to add additional columns that you think may help enrich the intelligence of your model, for example, the price of gasoline, GDP data, etc.
You have the freedom to choose any deep-learning or traditional machine-learning model of your choosing.
Only Tensorflow or PyTorch are allowed and we encourage you to use GPUs.
We recommend you split the data-set 80% for training and 10% validation and 10% testing
Performance Goal: Mean Absolute Error of 7.5% as measured against actuals prices for
time period 4 weeks out (from the last date in the training set given) to 12 weeks out.
Submission Deadline: April 15th, 2018. You’ll need to submit:
a. your source code in python (GitHub repo is fine)
b. README file with steps that include any framework/library dependencies and how to train and run inference.
c. a csv file with the predicted vs. actual on given test-set
d. csv file with predictions for daily prices 4 weeks out till 12 weeks out.
e. Performance numbers and analysis document
1st place: $2500 cash award + priority placement for a paid summer internship
2nd place: $1200 cash award + priority placement for a paid summer internship