Forecasting Macroeconomic Parameters with Deep Learning Neural Networks

Project Meeting(9/3/2018) Experiments: link

Project Meeting(2/3/2018) Topics to Discuss Seasonal Adjustment in Supervised Learning After performing some research on seasonal adjustment, I couldn’t figure out how to apply it in supervised learning(neural networks). Following is a discussion of seasonal adjustment approaches and some questions I have been wondering about. Seasonality and trend in a time series represent a dependence on the value of time. A classical time series decomposition is: y t = T t + S t + I t y_t = T_t + S_t + I_t y t ​ = T t ​ + S t ​ + I t ​ , where T t T_t T t ​ is the trend, S t S_t S t ​ is the seasonal component and I t I_t I t ​ is the irregular component. I t I_t I t ​ is independent of time and is stationary. In forecasting, we are only interested in I t I_t I t ​ and so it is important to remove S t S_t S t ​ and T t T_t T t ​ . This process is called seasonal adjustment. Seasonal Adjustment Methods There are generally two type of approaches for season...

Project Meeting(20/2/2018) Topics to Discuss Experimental Results Many to many results: link To do? Need to implement pre/post processing Need to develop RNN/CNN models Experiment with pre/post processing Experiment with optimiser Experiment with regularisation Write report

Project Meeting(13/2/2018) Topics to Discuss Experimental Results Many to one variables with 4 input lags feedforward network results here One to one variables with 4 input lags feedforward network results here

Project Meeting(6/2/2018) Topics to Discuss Experimental Results Some plots available here Proposed plan Tasks Competed

Project Meeting (30/1/2018) Topics to Discuss What is the objective? is it to find NN models better than ARIMA? How to compare NN and ARIMA models - based on prediction accuracy or forecast accuracy(how long forecast)? Experimentation workflow What data needs to be saved for model evaluation? NN input output size and how to recursively forecast Does it make sense to have an NN that has smaller input than output and how to compute the recursive forecast? Train validation and test sets Wilcoxon U test shows distributions of validation and test sets are different for some of the variables? Preprocessing Is data seasonally adjusted? Is it a good idea to remove NA rows so variables would have same length? Fourier transform for seasonality detection/adjustment? Quarterly Time-Series Forecasting With Neural Networks Finds that simple NN models with 0 or 1 hidden nodes perform best on time series and in particular macroecon...

Project Meeting (24/11/2017) Topics Discussed Data augmentation Data augmentation could be useful if the amount of data is not enough. Different techniques of the data augmentation discussed were: Interpolation Using equations to recursively predict one variable while performing one step ahead predictions for more. Adding random noise to existing data At this stage it is too early to determine weather or not data augmentation is required and could be useful. It remains a possibility for future experiments. Also transfer learning could be performed, training the network first on the augmented data and then retraining on the observed data. Transfer Learning Transfer learning could be applied using augmented/simulated data, data from other countries and data from EA countries for pretraining. The most promising option of the above would be using EA individual country data to pretrain and then train on the EA data. It is still early to decide we...