Time-Series Forecasting Pipeline with Automated Model Selection
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Solution Overview
Problem
Existing time-series forecasting systems are rigid and closed, limiting the opportunity to specify alternative modeling approaches, and require arduous programming, testing, and implementation of complex commands.
Innovation Solution
A pipeline system for time-series forecasting that includes a graphical user interface (GUI) for building and executing pipelines, allowing users to drag and drop operations, select model strategies, and automatically add or remove operations, enabling flexible and efficient forecasting with reusability of model strategies and pipelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a rigid and closed forecasting system is used, then the system structure is simple, but the adaptability and flexibility are limited
Solution Approach 1:
The forecasting system is divided into modular pipeline operations that can be independently selected, configured, and executed. Each operation represents a discrete step in the forecasting process, allowing users to build flexible pipelines by combining different operations without increasing overall system complexity.
Solution Approach 2:
The pipeline system provides a universal framework that can accommodate multiple forecasting models, evaluation metrics, and operational strategies through a single unified interface. This multi-functional approach enables the system to handle diverse forecasting tasks while maintaining structural simplicity.
2Reliability
If complex modeling approaches are implemented, then the forecasting capability is improved, but the programming and implementation difficulty increases
Solution Approach 1:
The system automatically performs model selection, parameter tuning, and pipeline optimization without requiring extensive manual programming. The automated evaluation framework selects the best forecasting models and configurations based on performance metrics, reducing the burden on users while maintaining high forecasting accuracy.
Solution Approach 2:
The system pre-configures multiple forecasting models and evaluation metrics that can be directly applied to time series data. This preliminary preparation eliminates the need for users to program complex modeling approaches from scratch, making advanced forecasting techniques accessible through simple pipeline configuration.
3Measurement precision
If multiple model strategies are evaluated, then the forecasting quality is improved, but the computational time and resources increase
Solution Approach 1:
The system evaluates multiple model strategies in parallel and selects the top-performing models based on evaluation metrics. Rather than exhaustively testing all possible combinations, the system performs partial evaluation focused on the most promising approaches, achieving high forecasting precision with reduced computational time.
Solution Approach 2:
The pipeline system maintains continuous evaluation of model strategies using pre-configured metrics, allowing for real-time comparison and selection of the best performing models. This continuous evaluation process efficiently utilizes computational resources while maintaining high forecasting accuracy.
Data Source
AI summary
A pipeline system for time-series data forecasting using a distributed computing environment is disclosed herein. In one example, a pipeline for forecasting time series is generated. The pipeline represents a sequence of operations for processing the time series to produce forecasts. The sequence of operations include model strategy operations for applying various model strategies to the time series to determine error distributions corresponding to the model strategies. The sequence of operations further include a model-strategy comparison operation for determining which of the model strategies is a champion model strategy for the plurality of time series based on the error distributions of the model strategies. The pipeline is executed to determine the champion model strategy for the time series.