Modular Forecasting Engine with Self-Tuning Algorithm Components
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Solution Overview
Problem
Developing a forecasting system that can accurately adapt to changing parameters and data sets in complex time-series forecasting applications is a costly and complex task, requiring significant reconfiguration and trial-and-error approaches, especially when additional or different parameters are introduced.
Innovation Solution
A method and tool that select and associate generically structured core and optional algorithm components with a framework, allowing each component to be individually tuned in a predetermined sequence, enabling the system to generate forecasts by conditioning data and providing a modular, customizable forecasting engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a forecasting system is customised for specific requirements by reconfiguring the system to determine which forecast algorithm generates the most accurate results, then forecasting accuracy is improved, but development complexity and cost increase significantly
Solution Approach 1:
The forecasting system is divided into separate, modular algorithm components that can be independently selected and combined. Each algorithm is a distinct unit that can be configured individually, allowing the system to be customized by assembling different components rather than reconfiguring the entire system, thus reducing development complexity while maintaining accuracy.
Solution Approach 2:
The system employs a universal framework that can accommodate multiple different forecast algorithms through a common interface. This multi-functional framework allows the same system structure to handle various forecasting requirements by simply changing the algorithm components, eliminating the need for separate custom systems for each requirement and reducing overall complexity.
2Adaptability or versatility
If the forecasting system reconfigures conventional systems each time additional parameters are introduced or deleted, then adaptability to new parameters is improved, but time consumption and development cost increase
Solution Approach 1:
The system uses a dynamic component selection mechanism where algorithm components can be added, removed, or modified based on the specific parameter requirements without affecting the overall system structure. This dynamic assembly allows the system to adapt to new parameters quickly by simply changing which components are active, rather than reconfiguring the entire system, thus reducing time loss while maintaining high adaptability.
3Reliability
If multiple forecast algorithms are combined to provide robust and accurate forecasting, then forecasting reliability is improved, but system complexity increases
Solution Approach 1:
Multiple forecast algorithms are merged into a unified framework that handles them through a common processing structure. The framework combines different algorithm components in a standardized way, allowing the system to achieve the reliability of multiple algorithms while maintaining the simplicity of a unified architecture, thus reducing perceived complexity despite having multiple algorithms.
Data Source
AI summary
A forecasting engine arranged to generate a forecast for a set of historic time-series data. The engine includes one or more one or more generically structured core algorithmic components providing a core function in a forecasting heuristic algorithm, and one or more generically structured optional algorithmic components providing an optional function in the forecasting heuristic algorithm. Each algorithmic component is individually tuned in a predetermined sequence, and the first algorithmic component in the sequence performs a tuning process on the set of historic time-series data. Subsequently, algorithmic components are tuned using time-series data conditioned by ail of the tuning processes previously performed in the predetermined sequence. The entire sequence of algorithmic components is arranged to collectively provide conditioned data which is used to generate a forecast.