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...