Predictive Model Scoring Using Time Series Forecasting
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Solution Overview
Problem
Existing systems for building and scoring predictive models for numerical attributes are inefficient due to the lack of consideration for external sources, use cases, and statistical influences, leading to complex configurations and reduced performance.
Innovation Solution
A system and method that select numerical variables based on use cases, statistical influences, and previous predictive samples, applying feature engineering and transformation, and performing time series forecasting or regression techniques to build and score predictive models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external sources, use cases, and statistical influences are not considered in predictive modeling, then the system complexity is reduced, but prediction accuracy deteriorates
Solution Approach 1:
The system segments the predictive modeling process into distinct modules: data acquisition module that collects from multiple sources including external sources, feature engineering module that applies domain-specific transformations, model training module that handles multiple algorithms, and evaluation module that assesses performance. This segmentation allows each module to specialize in specific aspects while maintaining overall system manageability despite the increased complexity of considering multiple factors.
Solution Approach 2:
The system introduces intermediary components such as feature engineering layers that transform raw data from multiple sources into meaningful features, and preprocessing modules that standardize data from diverse external sources. These intermediaries bridge the gap between complex multi-source data and the predictive models, enabling accurate predictions while managing the complexity through structured transformation pipelines.
2Reliability
If a user must configure special instructions for prediction, then the system becomes more controllable, but processing speed deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-configuring multiple predictive models with different algorithms and parameters during the model training phase. Configuration templates and predefined feature engineering pipelines are established in advance. When making predictions, the system can directly apply these pre-configured models without requiring real-time user configuration, thus maintaining controllability through pre-established parameters while achieving fast processing speeds during inference.
Solution Approach 2:
The system implements self-service capabilities through automated model selection that chooses the most appropriate predictive model based on the characteristics of the input data, and automated hyperparameter tuning that optimizes model parameters without user intervention. This self-service approach maintains system reliability through automated decision-making while eliminating the time cost of manual configuration, thereby improving processing speed.
3Measurement precision
If multiple data sources including external sources are used, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system implements a universal data acquisition and preprocessing framework that can handle multiple data sources including internal databases, external APIs, and various file formats through a unified interface. The feature engineering module applies universal transformation techniques that work across different data types and sources. This multi-functional approach enables the system to integrate diverse data sources effectively, improving prediction accuracy while managing processing complexity through standardized, reusable components.
4Measurement precision
If feature engineering and transformation are applied to extract features, then model performance is improved, but computational time increases
Solution Approach 1:
The system applies partial feature engineering by selecting and applying only the most relevant feature transformations based on the specific predictive task and data characteristics, rather than applying all possible transformations. The feature selection process identifies the subset of features that provide the most value, applying engineering transformations only to these selected features. This partial action approach maintains model performance by focusing on critical features while reducing computational time by avoiding unnecessary transformations.
Data Source
AI summary
System and method to build and score predictive model for numerical attributes are provided. The system includes a memory and a processing subsystem. The processing subsystem is configured to select one or more numerical variables from the plurality of data sets based on a plurality of parameters, to apply feature engineering and transformation on the one or more numerical variables, to perform time series forecasting on the one or more numerical variables based on the plurality of features extracted, to evaluate and select appropriate prediction technique based a regression technique based on a plurality of elements, to build a prediction model, to score the built prediction model based on the performed time series forecasting and an evaluated regression technique and to predict the built prediction model based on an obtained score. Further, the system uses the plurality of parameters and the prediction method to score and predict the prediction model.