Michael G McErlean

Title: Michael G McErlean: Innovator in Data Analytics and Machine Learning

Introduction

Michael G McErlean is a prominent inventor based in Sioux Falls, ND (US). He has made significant contributions to the field of data analytics and machine learning, holding two patents that showcase his innovative approach to forecasting commodities and assets.

Latest Patents

One of Michael's latest patents is focused on a curated sentiment analysis in a multi-layer, machine learning-based forecasting model using customized, commodity-specific neural networks. This data analytics platform is designed to forecast future states of commodities and other assets by processing both textual and numerical data sources. The platform features a multi-layer machine learning-based model that extracts sentiment from textual data through a natural language processing engine. It evaluates numerical data using time-series analysis and generates initial forecasts for the commodities or assets being analyzed. Additionally, the platform incorporates multiple applications of neural networks to develop augmented forecasts from further analysis of relevant information as it is collected. These applications include commodity-specific neural networks that continually develop taxonomies for processing commodity sentiment, as well as reinforcement learning, symbolic networks, and unsupervised meta-learning to enhance the overall performance and accuracy of the forecasts generated.

Career Highlights

Michael is currently associated with Agblox, Inc., where he applies his expertise in data analytics and machine learning. His work focuses on developing innovative solutions that leverage advanced technologies to improve forecasting accuracy in the commodities market.

Collaborations

Michael collaborates with notable colleagues, including Thomas N Blair and Alex A Kurzhanskiy, who contribute to the innovative projects at Agblox, Inc.

Conclusion

Michael G McErlean is a distinguished inventor whose work in data analytics and machine learning is paving the way for more accurate forecasting in the commodities sector. His innovative patents and collaborations highlight his commitment to advancing technology in this field.