Forecasting Macroeconomic Parameters with Deep Learning Neural Networks

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...

Topics Discussed Software Engineering Aspect of Project Need to have software engineering aspect of project. Potential ideas to be explored: Requirements engineering Unit Testing Planning - Gant Chart Report Structure Potential page limits for individual sections were discussed: Introduction: 2 Lit Review: 10 Problem Analysis: 5 Design and Implementation: 10 Results and Evaluation: 10 Conclusion: 3 Neural Network Initial experiments with a simple architecture need to be conducted. Experimentation workflow should be established in order to enable fast reliable prototyping. Unit tests can be used to ensure reliability of experiments. Statistical tests will be used to compare the performance of different architectures (ANOVA).  Tasks Completed Learn about stationarity, trends and seasonality. Analyse data - perform simple regression, figure out if it's stationary or not, identify trends and seasonality. Write up analysis. Read  Using R for Tim...

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...

Work done for past week Created project report outline in Latex. Created private GitHub repository for project. Started exploring Keras. Created reading list . The list includes all papers, books and past projects that I have read so far and will be continuously updated. Tasks for next week Read more on deep learning. Find research papers on: financial forecasting, time series prediction, deep learning, macroeconomic forecasting. Gain experience with Keras. Figure out correct formatting for project report (fonts, margins etc.) and modify document accordingly. Reading List Report Outline

Report Structure Introduction: Introduce topic in simpler terms. State goal, objectives and evaluation criteria. List chapters and explain structure of report. Only here it is acceptable to write about yourself(e.g. motivation to do this project). Goals should be to find out weather or not some hypothesis is true and it shouldn't be "make a better algorithm".  Literature Review Review what has been done so far in field of research. It is useful to make a table of all read papers, which summarises their most important points for the project. Problem Analysis Explain problem and argue why you have chosen solution (that you've chosen) based on the literature review. Software requirements go here. Design and Implementation Discuss the design and implementation of your solution. Results and Evaluation State results. Discuss what they mean and conclude which is the best one. Conclusion Draw conclusions. Can include speculations here (personal opinion). Suggest fu...