18152879. SYSTEMS AND METHODS FOR INDICATOR IDENTIFICATION simplified abstract (Adobe Inc.)
Contents
SYSTEMS AND METHODS FOR INDICATOR IDENTIFICATION
Organization Name
Inventor(s)
Iftikhar Ahamath Burhanuddin of Bangalore (IN)
Atanu R. Sinha of Kodbisanahalli (IN)
Saurabh Mahapatra of Sunnyvale CA (US)
SYSTEMS AND METHODS FOR INDICATOR IDENTIFICATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18152879 titled 'SYSTEMS AND METHODS FOR INDICATOR IDENTIFICATION
The method described in the abstract involves using machine learning to predict target time series data based on a variety of indicators, and then computing predictivity values to determine the relationships between these indicators.
- Machine learning model trained to predict target time series data based on candidate time series data
- Predictivity values computed to indicate predictive relationships between source indicators, intermediate indicators, and the target metric
- Display of candidate time series data corresponding to the indicators based on predictivity values
Potential Applications: - Financial forecasting - Stock market analysis - Predictive maintenance in manufacturing - Healthcare data analysis - Weather prediction models
Problems Solved: - Improving accuracy of predictions - Identifying key indicators for forecasting - Streamlining data processing and analysis
Benefits: - Enhanced predictive capabilities - Efficient data processing - Improved decision-making based on predictive insights
Commercial Applications: Title: "Predictive Analytics for Enhanced Decision-Making" This technology can be utilized in various industries such as finance, manufacturing, healthcare, and meteorology to improve forecasting accuracy and optimize decision-making processes.
Prior Art: Researchers can explore existing literature on machine learning models for time series prediction and predictive analytics to understand the advancements in this field.
Frequently Updated Research: Stay updated on the latest developments in machine learning algorithms for time series analysis and predictive modeling to enhance the effectiveness of this technology.
Questions about the technology: 1. How does this method improve upon traditional forecasting techniques? 2. What are the key factors to consider when selecting indicators for predictive modeling?
Original Abstract Submitted
One aspect of a method for data processing includes identifying target time series data for a target metric and candidate time series data for a plurality of indicators predictive of the target metric; training a machine learning model to predict the target time series data based on the candidate time series data; computing first through third predictivity values based on the machine learning model, wherein the first predictivity value indicates that a source indicator from the plurality of indicators is predictive of the target metric, the second predictivity value indicates that an intermediate indicator from the plurality of indicators is predictive of the target metric, and the third predictivity value indicates that the source indicator is predictive of the intermediate indicator; and displaying a portion of the candidate time series data corresponding to the intermediate indicator and the source indicator based on the first through third predictivity values.
