Exchange Rate Prediction with LSTM

This project utilizes a Long Short-Term Memory (LSTM) neural network to predict exchange rates. The model is trained on historical exchange rate data and can be used to make future predictions.

Project Overview

• The project uses PyTorch for implementing the LSTM model.
• Exchange rate data is normalized using Min-Max scaling.
• The LSTM model is trained on a portion of the data and then evaluated on the remaining test data.
• Users can input the number of days they want to forecast, and the model will provide future exchange rate predictions.

Getting Started

Prerequisites

• Python 3.x
• PyTorch
• pandas
• numpy
• matplotlib
• scikit-learn

Installation

Clone the repository:

git clone https://github.com/masanbasa3k/tr_usd_prediction.git
cd tr_usd_prediction

Install dependencies:

pip install -r requirements.txt

Usage

1. Run the training script to train the LSTM model:

python predict_model.py

2. Run the prediction script to make future forecasts:

python predict_next_days.py

Enter the number of days you want to forecast when prompted.

Results

The project includes visualizations of real exchange rate values, model predictions, and future forecasts.

Training Plot

Future Predictions Plot