Generative AI Agents with Customized Neural Networks for Commodity Forecasting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for forecasting commodity states over longer time horizons are unreliable due to their one-dimensional nature, failure to incorporate non-traditional data sources, and inability to process large amounts of data from diverse temporal contexts, leading to inaccurate predictions and a need for continuous filtering and absorption of growing information volumes, particularly from sources like social media and podcasts.
Innovation Solution
A multi-layer, machine learning-based data analytics platform that integrates supervised and unsupervised learning with neural networks to analyze both structured and unstructured data sources, including podcasts, for sentiment analysis and time-series modeling to enhance commodity forecasting, applying domain-specific knowledge and rules for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If classical statistical analyses and one-dimensional approaches are used for commodity forecasting, then the methods are simple and easy to implement, but the forecasting accuracy deteriorates for longer time horizons due to failure to incorporate non-traditional data sources
Solution Approach 1:
The patent combines multiple data sources including traditional structured data (pricing, weather, economic indicators) with unstructured non-traditional data (social media, news articles, podcasts) into a unified forecasting system. This merging of diverse data types enables the system to maintain both simplicity of implementation through integrated processing and high forecasting accuracy by leveraging comprehensive information from multiple sources.
Solution Approach 2:
The forecasting system is designed to handle multiple types of data sources and analysis methods within a single platform. It can process structured numerical data, unstructured text data, audio transcripts, and visual content simultaneously, making the system universally applicable to various forecasting needs while maintaining accuracy across different time horizons and commodity types.
2Reliability
If machine learning models process large amounts of data from diverse sources, then forecasting accuracy improves, but the complexity of data processing and filtering increases
Solution Approach 1:
The patent segments the data processing workflow into distinct modules: data collection from multiple sources, preprocessing and cleaning, feature extraction, model training, and forecasting. Each module handles specific tasks independently, which reduces overall system complexity while enabling accurate processing of large diverse datasets. The segmentation allows parallel processing and independent optimization of each component.
Solution Approach 2:
The system introduces intermediary components such as data preprocessors, feature extractors, and transformers that mediate between raw diverse data sources and the core machine learning models. These intermediaries standardize and simplify data representations, reducing the complexity burden on the main forecasting algorithms while preserving accuracy through systematic data transformation pipelines.
3Adaptability or versatility
If continuous filtering and absorption of growing information volumes is performed, then the system adapts to current data, but the time and computational resources required increase
Solution Approach 1:
The system implements periodic batch processing combined with selective real-time updates. Instead of continuously processing all incoming data, it periodically retrained models on accumulated data while using lighter-weight filtering for real-time adaptability. This periodic action reduces computational overhead while maintaining system adaptability to changing conditions.
Solution Approach 2:
The patent applies partial processing strategies where not all incoming data is processed in full detail. Instead, it uses selective filtering, sampling, and prioritization to process only the most relevant portions of data streams. This partial action approach maintains adaptability by focusing on critical information while significantly reducing processing time and computational resource requirements.
Data Source
AI summary
A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural networks to develop augmented forecasts from further analysis of relevant information as it is collected. These include commodity-specific neural networks designed to continually develop taxonomies used to process commodity sentiment, and applications of reinforcement learning, symbolic networks, and unsupervised meta learning to improve overall performance and accuracy of the forecasts generated.