Project Meeting (10/11/2017)

Topics Discussed

Experiments performed

Discussed the experiment results and how to interpret them. Even if overall forecast is not very accurate, determining the direction in which the variable will go is still valuable. Should produce a plot (3D / heatmap) of hyper-parameter grid search.

Results from this paper

Useful to see “recursive” prediction results to compare with the ones obtained with neural networks. A prediction of two years ahead is very good. Maybe possible to motivate use of deep learning for forecasting based on results from non-linear equation discovery models.

Further experiments

Should create an ARIMA and use it as a benchmark to compare results from neural nets. Further univariate variable experiments should be performed using seasonal and trend decomposition on input data and first order difference.

Tasks Competed

• [x] Perform experiments with simple NN.
• [x] Establish experimentation workflow.

Tasks for Next Week

• [] Create benchmark ARIMA model.
• [] Experiment with first difference univariate models .
• [] Experiment with decomposition univariate models.
• [] Create plots from hyper-parameter grid search tables.
• [] Read deep learning book on RNNs.
• [] Read more papers.
• [] Write up literature review outline in Latex and further break it down into smaller topics.
• [] Figure out correct formatting for project report (fonts, margins etc.) and modify document accordingly.