Forecasting Macroeconomic Parameters with Deep Learning Neural Networks
Project Blog
The progress of the project was tracked via a Blog. The thesis and other delivarables can be found there: https://deepforecastproject.blogspot.com/
Usage
Set EXPERIMENTS_DIR in config.json. This is where experimental results will be stored.
Run ARIMA experiments:
python src/arima/arima.py
Run persistance baseline model:
python src/persistence/persistence.py
Neural network experiments
In order to run the automatic model selection use:
python src/neuralnets/models/main.py
usage: main.py [-h] -m {mlp,lstm,all} -d {yes,no,both} [-a] [-b] [-c]
optional arguments:
-h, --help show this help message and exit
-m {mlp,lstm,all}, --model {mlp,lstm,all}
Which models to run.
-d {yes,no,both}, --diff {yes,no,both}
Weather or not to use first order differencing
-a, --one-to-one This argument only applies to mlp. Run one-to-one
architecture
-b, --many-to-one This argument only applies to mlp. Run many-to-one
architecture
-c, --many-to-many This argument only applies to mlp. Run many-to-many
architecture
The search space of NN hyper parameters and hyper parameter optimisation algorithm can be changed in src/neuralnets/models/run_models.py. Default hyper parameter optimisation is Particle Swarm Optimisation. Alternatives are Grid Search Optimisation and Random Search Optimisation.
Searched parameters can be added/removed in the model specific files 'src/neuralnets/models/mlp.py' and src/neuralnets/models/lstm.py.
The hyperparameter optimisation package is more or less self contained and can be used independantly from the NN models. It can be found in src/neuralnets/hypersearch/optimizers.
src/neuralnets/forecast_model/ contains a scikit-learn like wrapper implementation of MLP and RNN models for forecasting time series.