Forecasting Macroeconomic Parameters with Deep Learning Neural Networks

Machine Learning Strategy Notes Notes on the coursera course Structuring Machiene Learning Projects I recently started. Intro Orthogonalisation - varying one hyper parameter affect exactly one metric. Examples of orthogonal hyperparameters: Metric Hyperparameter Fit on training set network size, optimisation algorithm Fit on validation set regularisation, bigger training set Fit on test set bigger validation set Early stopping is not very orthogonal because it affect both training and validation fit. Defining a goal Evaluation metric It is a good practice to have a single number evaluation metric because it makes it easier to compare different models. This might mean combining several evaluation metrics using an average/harmonic mean or other approach. Satisficing evaluation metric Evaluation metric which needs to be only below within a certain interval. As long as the metric is within the specified interval there is no...

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 b...

Attached to this post are the results from initial experiments with the EA data set. The csv files contain details of all runs made with the specific variable. The png files are plots of the prediction of the best model found for the particular input. The numbers in file names indicate how many lags were used for the experiment(e.g. 2 means inputs were and and output was )

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...