Who is interested in joining the second Time Series Learning Challange:
“Predict the S&P500?”
We have:
~ A draft of the challenge
~50 years of data
~ A simple notebook to load and split the data.
~ A fairly large sample notebook for data pre-processing
There are a lot of time series data out there: Sensor data, climate data, IoT data and you name it. However, predicting a time series correctly and reliably is often challenging in practice due to the inherent complexity with the stock market being notoriously difficult to predict. Can we do it? So let’s tackle it and crack the S&P prediction challenge together.
Some crazy ideas:
- CoordConv (Paper) (Repo) (Ueber)
- Pytorch + pyro
- Deep Neural Network Ensembles for Time Series Classification: SOTA
- Bayesian Convolutional Neural Network
- Time series feature engineering with tsfresh
- Transfer learning
- And more, please add in the comments
If you have any questions, please don’t hesitate to post your question!
Special thanks to Oguiza for leading the first one and inspiring the formation of the second one.