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Project on Foreign Exchange Forecasting, for the Μ401 - Deep Neural Networks course, NKUA, Fall 2022.

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Forex Forecasting

This repository contains models designed to forecast foreign exchange currencies using Neural Networks. Two hybrid methods are presented (SCNN_model and es-rnn) while a purely statistical one (V-AR-model) was used as a benchmark. A detailed analysis of implementations, methodology, and results can be found in the report.

The project was carried out as a key part of the curriculum for the 'Μ401 - Deep Neural Networks' course, as taught at the National and Kapodistrian University of Athens (NKUA), during the Fall of 2022.

Project Structure

$PROJECT_ROOT
¦
+-- SCNN_model 
¦   # Forecasting using a simple Exponential Smoothing
¦   # and Convolutional Neural Networks
¦
+-- V-AR-model 
¦   # Forecasting using Vector Autoregression
¦   # (used as a benchmark)
¦
+-- es-rnn 
¦   # Forecasting using Holts-Winters and Recurrent Neural Networks
¦
+-- presentation 
¦   # Presentation summarising implementations, results, and conclusions 
¦
+-- report 
    # Comprehensive report detailing implementations, results, and conclusions

Algorithms

Before diving into the individual methodologies, ensure you have the necessary dependencies installed. Each implementation directory (SCNN_model, V-AR-model, es-rnn) contains its own README.md file, detailing specific requirements and dependencies; so have a look at it before running the respective model.

Data Source

The data is sourced from the European Central Bank and can be downloaded using a script of the repo. The dataset contains historical exchange rates of various currencies against the Euro, and is published around 16:00 CET.

These rates represent the official currencies of non-euro area Member States of the European Union and world currencies with the most liquid active spot FX markets. The dataset can be downloaded from here, and an overview can be seen bellow.

Date USD JPY BGN ... THB ZAR
2023-09-15 1.0658 157.50 1.9558 ... 38.145 20.2968
2023-09-14 1.0730 158.13 1.9558 ... 38.387 20.3109
2023-09-13 1.0733 158.28 1.9558 ... 38.397 20.3300
... ... ... ... ... ... ...
1999-01-04 1.1789 133.73 NaN ... NaN 6.9358

Authors

License

This project is licensed under the MIT License - see the LICENSE file for details.