Time Series Forecasting Composite Model Generation
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Solution Overview
Problem
Current analytical methods for businesses are inadequate in accounting for external factors and generating meaningful forecasts, being manual, time-consuming, and limited by requiring specialized expertise, thus failing to provide accurate and user-friendly forecasts that incorporate internal and external data effectively.
Innovation Solution
Systems and methods for generating forecasts using time series data by normalizing and combining datasets from various sources, including external data, to create composite models that automatically update, allowing users to select indicators, set weights, and adjust time offsets for improved accuracy and usability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual statistical analysis methods are used, then analysis can be performed with existing tools, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual statistical analysis with automated computational systems that use machine learning algorithms and computer-based data processing to generate forecasts, eliminating the need for manual calculation while maintaining or improving accuracy
Solution Approach 2:
The system performs self-service by automatically collecting, processing, and analyzing data without requiring manual intervention, with the computational system independently generating forecasts through automated algorithms
2Measurement precision
If segment-specific user expertise is required for statistical analysis, then analysis can be customized to specific needs, but the ease of operation substantially decreases
Solution Approach 1:
The patent creates a universal forecasting system that can handle multiple types of data and analysis requirements through a single integrated platform, making advanced statistical capabilities accessible to users without specialized expertise while maintaining precision through automated algorithms
3Adaptability or versatility
If current analytical methods are used, then existing processes can be maintained, but they remain incomplete and inadequate for complex business landscapes
Solution Approach 1:
The patent merges multiple data sources including internal organizational data with external factors such as demographic, economic, and environmental data into a unified forecasting model, enabling comprehensive analysis of complex business landscapes while managing system complexity through integrated architecture
Data Source
AI summary
The present invention relates to systems and methods for forecasting using time series datasets. A composite may be generated by receiving datasets, normalizing them, and receiving formula configurations in order to combine the datasets together. The transformation of a dataset may be restricted if the accuracy of the transformation would be decreased, and if no suitable alternate dataset is available. A forecast may be generated using selected forecast type, calculation type, cutoff period, pre-adjustment, post-adjustment, indicators, and selected weights and offsets for the indicators. The forecast analysis may be updated by locking the time domain for one or more of the indicators. Forecast results may be outputted to a spreadsheet or other system utilizing add-ins. Any composite or forecast generated may be stored within a model repository for later use as an indicator.