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Stuff to do/ideas I want to try:
- QLasso implemented as in article. This is nice because it represents a benchmark as a thought-through model, and we can compare results to know if our implementation is working. https://web.stanford.edu/~bayati/papers/edwait.pdf
- LSTM and/or some other recurrent network. Since these networks have "memory" it would be interesting if one can feed the events more or less directly to the models. Are said to require huge amount of data for training but we might have enough.
- Nonlinear autoregression. Works well for multivariate time series prediction of electric load here http://cs229.stanford.edu/proj2016/report/GaoWuLiu-WindPowerAndElectricLoadForecasting-report.pdf
- Gradient boosting. Works well for multivariate time series prediction of sales here http://cs229.stanford.edu/proj2015/218_report.pdf
- Some feature reduction method. QLasso has this built in. Haven't read about this in the context of multivariate time series yet.
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