Temporal Convolutional Networks (TCN) Model for Financial Predictions

This repository contains an implementation of an Temporal Convolutional Networks (TCN) model, specifically designed for predicting the prices of financial instruments such as currencies, stocks, and cryptocurrencies. The Temporal Convolutional Networks (TCN) algorithm leverages gradient boosting techniques, enabling it to capture intricate patterns in price movements and handle various dataset characteristics effectively. This approach enhances the accuracy and robustness of price forecasts across various datasets.

This is the original code sample for the Temporal Convolutional Networks (TCN) model. Explore my GitHub repository for additional models and implementations that cater to different financial prediction needs.

Performance Metrics

BTC-USD (Bitcoin)

Metric	Open	High	Low	Close
Mean Squared Error	0.000284	0.000390	0.000406	0.000477
Mean Absolute Error	0.010150	0.011729	0.013171	0.013690
R-squared	0.994994	0.993189	0.992629	0.991648
Median Absolute Error	0.005378	0.005961	0.007799	0.007762
Explained Variance Score	0.996156	0.994318	0.994150	0.992889

GC=F (Gold Futures)

Metric	Open	High	Low	Close
Mean Squared Error	0.000436	0.000562	0.000551	0.000444
Mean Absolute Error	0.016647	0.018161	0.018460	0.016801
R-squared	0.975805	0.968967	0.969793	0.975367
Median Absolute Error	0.013984	0.013627	0.016061	0.014354
Explained Variance Score	0.976963	0.969709	0.970969	0.976345

EURUSD (Euro/US Dollar)

Metric	Open	High	Low	Close
Mean Squared Error	0.000369	0.000460	0.000482	0.000577
Mean Absolute Error	0.014165	0.015890	0.016990	0.018085
R-squared	0.993684	0.992230	0.991744	0.990154
Median Absolute Error	0.010271	0.011423	0.013464	0.013742
Explained Variance Score	0.995478	0.993961	0.993813	0.992083

GSPC (S&P 500 Index)

Metric	Open	High	Low	Close
Mean Squared Error	0.000464	0.000647	0.000583	0.000831
Mean Absolute Error	0.016751	0.020288	0.019314	0.023397
R-squared	0.992203	0.989640	0.990148	0.986636
Median Absolute Error	0.012966	0.017563	0.016941	0.020840
Explained Variance Score	0.994433	0.992266	0.992138	0.989045

Related Websites

Predict Price

Free AI-powered short-term (5/10/30 days) and long-term (6 months/1/2 years) forecasts for cryptocurrencies, stocks, ETFs, currencies, indices, and mutual funds.

Magical Prediction

Get free trading signals generated by advanced AI models. Enhance your trading strategy with accurate, real-time market predictions powered by AI.

Magical Analysis

Discover free trading signals powered by expert technical analysis. Boost your forex, stock, and crypto trading strategy with real-time market insights.

About This Project

This Temporal Convolutional Networks (TCN) model is an initial implementation, released for public use. The project demonstrates the potential of deep learning models for financial predictions. While this repository focuses on Temporal Convolutional Networks (TCN), I have also utilized other models, the code for which is available on my GitHub[https://github.com/taleblou/].

How to Use

1. Clone this repository.
2. Install the required libraries: pip install -r requirements.txt
3. Prepare your dataset and follow the instructions in the notebook or script.
4. Run the model and evaluate its performance using the provided metrics.

License

This project is open-source and available for public use under the MIT License. Contributions and feedback are welcome!