Forecasting-as-a-Service System for Time Series Data
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Solution Overview
Problem
Current information handling systems, particularly in forecasting, face challenges in scaling to handle large datasets and providing user-friendly interfaces for business users, as they require specialized knowledge and resources, limiting the ability to efficiently process and analyze time series data.
Innovation Solution
The implementation of a Forecasting-as-a-Service (FaaS) system that uses common time series algorithms to generate forecasts in a single step, allowing for the automatic selection and execution of multiple models, data preprocessing, and visualization, enabling efficient handling of massive data volumes and user-friendly interaction for business users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional forecasting systems are used to handle large datasets, then data processing capability is improved, but system complexity and resource requirements increase
Solution Approach 1:
The system segments the forecasting process into distinct modular components: data ingestion module, model selection module, forecasting engine, and visualization module. Each module handles specific tasks independently, allowing the system to scale to large datasets without proportionally increasing overall complexity. The segmentation enables parallel processing and independent optimization of each component.
Solution Approach 2:
The forecasting system is designed as a universal platform that can handle multiple data types, time series configurations, and forecasting algorithms through a single integrated architecture. The system provides multi-functional capabilities including data preprocessing, automated model selection, ensemble forecasting, and visualization, eliminating the need for separate specialized systems for different forecasting scenarios.
2Measurement precision
If specialized forecasting tools are used, then forecasting accuracy is improved, but ease of operation deteriorates as specialized knowledge is required
Solution Approach 1:
The system implements automated model selection and configuration capabilities that allow the forecasting engine to automatically choose appropriate algorithms and parameters based on the characteristics of the input data. This self-service functionality eliminates the need for users to have specialized knowledge of forecasting methodologies while maintaining high accuracy through algorithmic optimization.
Solution Approach 2:
The system introduces an intelligent intermediary layer between the user and the complex forecasting algorithms. This intermediary automatically translates user-friendly inputs into appropriate model configurations and algorithm selections, bridging the gap between simple user interaction and complex computational processes while preserving forecasting accuracy.
3Measurement precision
If multiple forecasting models are evaluated, then model selection accuracy is improved, but time consumption increases
Solution Approach 1:
The system evaluates multiple forecasting models simultaneously using parallel processing, and automatically selects the top-performing models based on validation metrics. By implementing partial evaluation (assessing only the most promising models in detail) and using excessive action (quick preliminary screening of many models), the system achieves accurate model selection without proportionally increasing time consumption.
Solution Approach 2:
The model evaluation process is designed to run continuously and efficiently, with models being assessed in parallel rather than sequentially. The system maintains continuous optimization by automatically re-evaluating models when new data becomes available, reducing the time loss associated with periodic manual model selection while maintaining high selection accuracy through persistent performance tracking.
Data Source
AI summary
An information handling system comprising a data store is configured to store time series data and a processor. The processor is configured to acquire data, the data including time series data, isolate one or more time series from the data, assigning a unique time series identifier to each time series, and storing the time series and the time series identifiers in the data store, forecast additional time points for the one or more time series using a plurality of models, determine a fit statistic for each model for each time series, select a preferred model for each time series based on the fit statistics of the models for the time series, and provide a forecast to a user for each time series.