Forecasting Macroeconomic Parameters with Deep Learning Neural Networks

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...

Data Analysis Exploratory data analysis was performed as agreed upon in last weeks meeting. The report can be seen below.

Topics Discussed Data Variables Meaning of variables in the data set CPI: Consumer price index. GDP: Gross domestic product UR: unemploymeent rate. IR Policy Rate - Interest rate (Possibly inflation subtracted from  nominal interest rate) LR10: Possibly 10 year loan rate. LR10 - IR: IR subtracted from LR10. Exrate Euro for 1 USD - exchange rate b/w euro and dollar. Wheather or not data has been preprocessed in any way - about to find out. Data analysis and preprocessing Should check if data is stationary or not. Identify any trend and seasonality. Test significance of each variable using a benchmark architecture (simple MLP). Explore different preprocessing techniques and if they improve performance on benchmark architecture. Longer term question - do we need more data?  Report Possible structure of Problem Analysis? Data - what kind and where froerform m? Stationarity, Seasonality, Trend Neural network architecture Tasks Completed Rea...

Topics discussed Literature Review Basic Structure (for now) Macroeconomics Importance of macroeconomic forecasting. Analysis of the data set and different parameters. Time Series Analysis of standard forecasting approaches. Discussion on seasonality, stationarity and other characteristics of time series data. Deep Learning Discussion of the emerging field of deep learning - advantages and disadvantages. Analysis of deep learning approaches. Macroeconomic Forecasting using Deep Learning Connecting the above topics and analysis on the work done in the field so far. Missing Data Dealing with missing values of parameters in the data should be done with caution. Possible solutions include leaving out data points with missing values or imputation techniques to estimate missing values. Leaving out data points may cause disruption in the continuity ofe the time series. Reading Resources Using R for Time Series Analysis  - Classical methods for time series analysis a...