This repository contains the research and implementation details of the Deep Nexus One (DN1) machine learning model, originally developed in 2018. The project represents a novel approach to applying a proprietary machine learning model initially designed for financial market prediction to non-financial datasets.
- Model: Deep Nexus One (DN1) Machine Learning Model
- Original Purpose: Predicting direction and magnitude in financial markets
- Research Objective: First formal experiment testing DN1 on non-financial data
- Year of Research: 2018
- Real-time actionable output for both machine and human interpretation
- Adaptable predictive model originally developed for financial markets
- Innovative cross-domain application to non-financial datasets
The study conducted a comprehensive comparison of the Deep Nexus One (DN1) model against other deep learning neural network models trained on the same dataset. The research aimed to explore the model's predictive capabilities beyond its original financial market application.
This project is licensed under the Apache License, Version 2.0 (the "License"); you may not use this code except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific details of the License.
The original training and testing datasets used in this research are proprietary and were provided by Ohio University. These datasets are not included in this repository and cannot be shared publicly.
If you use this research or code in your work, please cite the original paper.
Repository initialized in 2024, based on research conducted in 2018.