Automated Time-Series Feature Extraction for Static Forecast Models
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Solution Overview
Problem
Existing time-series forecasting methods require significant domain expertise for manual feature engineering and substantial computational resources, limiting their accessibility and accuracy for non-experts across diverse domains.
Innovation Solution
An automated method for time-series forecasting that transforms single-value datasets into multiple-feature datasets using static machine learning models, enabling automated feature extraction and model training, reducing the need for domain expertise and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional time-series forecasting methods (ARIMA, LSTM) are used, then temporal dependencies can be captured, but significant domain expertise and manual feature engineering are required
Solution Approach 1:
The system performs automated feature engineering where the model automatically identifies and extracts relevant features from raw time-series data without requiring manual domain expertise. The feature engineering process is self-executing through the automated pipeline that transforms raw data into model-ready features using statistical calculations and pattern recognition algorithms.
Solution Approach 2:
The forecasting process is divided into distinct automated modules: data preprocessing, feature extraction, model training, and prediction. Each module handles specific tasks automatically, breaking down the complex forecasting process into manageable segments that execute without manual intervention, thereby improving ease of use while maintaining accuracy.
2Reliability
If LSTM networks are used for complex time-series problems, then long-term dependencies can be learned, but extensive computational resources and neural network expertise are required
Solution Approach 1:
The system employs lightweight machine learning models that require minimal computational resources compared to heavy LSTM networks. These simpler models are trained automatically on processed features and can be quickly deployed and replaced, reducing the need for extensive computational infrastructure while maintaining practical forecasting accuracy for many applications.
Solution Approach 2:
The system extracts only the essential temporal patterns and features from the time-series data through automated feature engineering, rather than using the entire raw dataset with complex LSTM architectures. This extraction of key temporal dependencies into compact feature representations reduces computational requirements while preserving the most important information for forecasting.
3Reliability
If manual feature engineering is performed, then model performance can be optimized, but the process is time-consuming and error-prone
Solution Approach 1:
The automated feature engineering pipeline self-executes through multiple stages including data preprocessing, statistical feature extraction, temporal pattern recognition, and model training. The system automatically identifies relevant features and optimizes model parameters without requiring manual domain expertise, significantly reducing the time investment while maintaining or improving model performance through systematic automation.
Solution Approach 2:
The system incorporates automated model evaluation and hyperparameter tuning that uses performance feedback to iteratively improve feature engineering and model selection. This closed-loop approach automatically adjusts and optimizes features based on validation results, ensuring high model performance without manual intervention and reducing the time required for manual trial-and-error optimization.
4Ease of operation
If static regression models are used, then simplicity and efficiency are achieved, but they cannot be used for time-series problems without manual feature engineering
Solution Approach 1:
The system combines static regression models with automated feature engineering that self-executes to transform raw time-series data into appropriate input features. The automated pipeline handles data preprocessing, feature extraction, and transformation, allowing simple static regression models to effectively solve time-series forecasting problems without requiring manual feature engineering complexity while maintaining simplicity in the core modeling approach.
Data Source
AI summary
Existing methods for time series forecasting are based on models specifically designed to handle temporal dependencies with domain expert assistance to engineer the features that the model will used for training. Disclosed herein is a method and process for time-series data forecasting and imputing using automated feature extraction algorithms and static machine learning models. The method and process disclosed herein includes algorithms for automated feature extraction from time series signals that contain single time-aware variable. The processes implemented in software described herein consists of an end-to-end pipeline for generation of machine learning training dataset, automated training procedure with static machine learning models, and deploying the model to make forecasts or impute missing time-series data. In essence, this pipeline enables non-domain experts to apply the model to time-series data regardless of data domain, by transferring the time-series problem from temporal domain to static (features) domain.