Time-Series Ensemble Forecasting Across Horizons With User-Selected Models
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Solution Overview
Problem
Existing computer-based technologies for time-series forecasting fail to account for unexpected contributing factors, lack transparency, require specialized knowledge, and lack scalability, leading to inaccurate and complex forecasting processes.
Innovation Solution
A software tool that generates an ensemble model blending forecasts from multiple time-series models of different types for varying timeframes, allowing user-friendly configuration and execution, and provides transparent and scalable forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single time-series model is used for forecasting, then the model structure is simple, but the forecasting accuracy is insufficient due to inability to account for unexpected contributing factors
Solution Approach 1:
The patent combines multiple time-series models of different types (e.g., ARIMA, exponential smoothing, neural networks) into an ensemble model that collectively forecasts time-series data. This merging allows the system to capture various patterns and unexpected contributing factors that a single model would miss, thereby improving forecasting accuracy while managing complexity through systematic integration.
Solution Approach 2:
The ensemble model functions as a composite structure where different model types (analogous to different materials) are combined to create a more robust forecasting system. Each model type contributes its unique strengths, similar to how composite materials combine different substances to achieve superior properties that individual materials cannot provide alone.
2Reliability
If multiple time-series models are combined to improve accuracy, then the forecasting reliability improves, but the device complexity increases
Solution Approach 1:
The ensemble model is segmented into distinct model types, each responsible for specific aspects of time-series forecasting. This segmentation allows the complex system to be broken down into manageable components that can be independently trained, evaluated, and maintained, reducing the operational complexity despite the increased number of models.
Solution Approach 2:
The ensemble model framework is designed to be universal, accommodating multiple types of time-series models through a common architecture. This multi-functionality allows the same framework to integrate different model types (ARIMA, exponential smoothing, neural networks) without requiring separate systems, thereby managing complexity through standardized interfaces and processes.
3Measurement precision
If specialized time-series forecasting technologies are used, then the forecasting precision improves, but the ease of operation deteriorates due to requiring specialized knowledge
Solution Approach 1:
The system incorporates automated model selection, training, and evaluation capabilities that allow it to self-configure and optimize without requiring user expertise in time-series forecasting. The ensemble model automatically selects appropriate model types, tunes parameters, and blends forecasts, enabling users with minimal technical knowledge to achieve high forecasting precision.
Solution Approach 2:
The patent introduces an intermediary layer (the ensemble model framework) that translates complex model operations into user-friendly interfaces. This intermediary handles the complexity of multiple models, parameter tuning, and forecast blending, while presenting simplified controls and interpretations to users, thereby decoupling forecasting precision from operational complexity.
4Adaptability or versatility
If existing forecasting technologies are used, then implementation is straightforward, but the adaptability to different conditions and timeframes is limited
Solution Approach 1:
The ensemble model is designed to be dynamic, automatically adapting to different timeframes and data conditions. The system dynamically selects and weights models based on the specific forecasting horizon and data characteristics, allowing it to adjust its composition and behavior without manual reconfiguration, thereby achieving high adaptability while managing complexity through automated decision-making.
Data Source
AI summary
A computing platform is configured to (i) generate first and second sets of time-series models for forecasting values of a time-series variable for respective and different first and second timeframes, the first and second sets each comprising models of different types, (ii) (ii) receive configuration data for an ensemble model that identifies a user-selected group of time-series models to be included in the ensemble model, where the user-selected group includes at least one model from the first set and at least one model from the second set, (iii) based on the received configuration data, construct the ensemble model from the user-selected group of time-series models, where the ensemble model is configured to blend the forecast values of the time-series variable that are predicted by the user-selected group of time-series models, and (iv) utilize the ensemble model to predict a given sequence of forecast values for the time-series variable.