17746710. METHODS AND SYSTEMS FOR GENERATING FORECASTS USING AN ENSEMBLE ONLINE DEMAND GENERATION FORECASTER simplified abstract (Dell Products L.P.)
Contents
- 1 METHODS AND SYSTEMS FOR GENERATING FORECASTS USING AN ENSEMBLE ONLINE DEMAND GENERATION FORECASTER
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHODS AND SYSTEMS FOR GENERATING FORECASTS USING AN ENSEMBLE ONLINE DEMAND GENERATION FORECASTER - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Original Abstract Submitted
METHODS AND SYSTEMS FOR GENERATING FORECASTS USING AN ENSEMBLE ONLINE DEMAND GENERATION FORECASTER
Organization Name
Inventor(s)
Arun Kumar Venkitaraman of Bangalore (IN)
Renold Raj Devaraj of Bangalore (IN)
METHODS AND SYSTEMS FOR GENERATING FORECASTS USING AN ENSEMBLE ONLINE DEMAND GENERATION FORECASTER - A simplified explanation of the abstract
This abstract first appeared for US patent application 17746710 titled 'METHODS AND SYSTEMS FOR GENERATING FORECASTS USING AN ENSEMBLE ONLINE DEMAND GENERATION FORECASTER
Simplified Explanation
The abstract describes a method for generating final prediction data using a prediction model. Here are the key points:
- The method involves generating validation prediction data using a prediction model.
- It identifies unexplained variance data within the validation prediction data.
- Initial prediction data is generated using the prediction model.
- Prediction trend data is isolated from the initial prediction data.
- The final prediction data is obtained by summing the prediction trend data with the unexplained variance data.
- A user can provide a constraint for the final prediction data.
- The method determines if the final prediction data satisfies the constraint.
- If the constraint is satisfied, the method indicates to the user that the final prediction data meets the constraint.
Potential Applications
- This method can be applied in various fields that require prediction models, such as finance, weather forecasting, and sales forecasting.
- It can be used in data analysis and decision-making processes where accurate predictions are crucial.
Problems Solved
- The method addresses the issue of unexplained variance in prediction data, which can affect the accuracy and reliability of predictions.
- By isolating and incorporating the unexplained variance data into the final prediction, the method improves the overall accuracy of the predictions.
Benefits
- The method provides a more accurate and reliable prediction by considering both the prediction trend and the unexplained variance.
- Users can set constraints to ensure that the final prediction data meets specific requirements.
- It allows for better decision-making based on more accurate predictions.
Original Abstract Submitted
A method for generating final prediction data, that includes generating, by a prediction generator, validation prediction data using a prediction model, making a first determination that the validation prediction data includes unexplained variance data, in response to making the first determination, isolating unexplained variance data from the validation prediction data, generating initial prediction data using the prediction model, isolating prediction trend data from the initial prediction data, obtaining the final prediction data by summing the prediction trend data with the unexplained variance data, receiving, from a user, a constraint, making a second determination that the final prediction data satisfies the constraint, and based on the second determination, indicating, to the user, that the final prediction data satisfies the constraint.
