Automated Time-Series Forecasting Pipeline Selection
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Solution Overview
Problem
Current automatic time-series analysis products are limited to handling homogeneous pipelines and lack a joint optimizer for concurrently optimizing multiple heterogeneous pipelines, failing to effectively utilize exogenous and imputed data for robust forecasting.
Innovation Solution
An automated system and method for generating and evaluating multi-variate time-series models with and without exogenous data, using data imputation, transformation, and pipeline generation libraries to create and rank optimized forecasting pipelines, while masking imputed data to ensure robust scoring and reducing resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple heterogeneous pipelines are optimized concurrently using exogenous and imputed data, then prediction accuracy is improved, but computational resources and system complexity increase
Solution Approach 1:
The system segments the optimization process into distinct phases: data imputation phase, pipeline generation phase, evaluation phase, and selection phase. Each phase handles specific tasks independently, allowing complex heterogeneous pipeline optimization to be broken down into manageable segments that can be processed sequentially or in parallel as appropriate.
Solution Approach 2:
The system performs preliminary data imputation and validation before pipeline optimization begins. By pre-processing the data and establishing quality thresholds in advance, the system reduces the computational burden during the actual optimization phase and ensures that only high-quality imputed data is used in pipeline training and evaluation.
2Reliability
If exogenous and imputed data are used in forecasting pipelines, then predictive power is enhanced, but data processing time and computational load increase
Solution Approach 1:
The system applies partial imputation by selectively imputing only the necessary missing data points that are critical for pipeline optimization, rather than imputing all missing data uniformly. This approach maintains predictive power while reducing unnecessary computational overhead from imputing redundant or low-impact data points.
Solution Approach 2:
The system dynamically adjusts imputation parameters and data processing thresholds based on the specific characteristics of the input data and the requirements of different pipeline types. By changing parameters such as imputation method selection, data sampling rates, and evaluation thresholds, the system optimizes the balance between predictive power and processing time for each specific forecasting scenario.
3Reliability
If a joint optimizer is implemented for multiple heterogeneous pipelines, then forecasting robustness is improved, but computational resource requirements increase
Solution Approach 1:
The system merges the optimization of multiple heterogeneous pipelines into a single joint optimization framework that shares common computational resources, data preprocessing steps, and evaluation mechanisms. By combining pipelines that share similarities and coordinating their optimization processes, the system achieves robust forecasting across diverse pipeline types while reducing redundant computational resource consumption.
Solution Approach 2:
The joint optimizer is designed with universal functionality that can handle multiple types of forecasting pipelines (regular, exogenous, imputed) through a unified interface and common optimization algorithms. This multi-functional approach allows the same computational infrastructure to serve multiple pipeline types simultaneously, reducing overall resource requirements compared to having separate optimizers for each pipeline type.
Data Source
AI summary
Selecting a time-series forecasting pipeline by receiving target variable time-series data and exogenous variable time-series data, generating a regular forecasting pipeline comprising a model according to the target variable time-series data, generating an exogenous forecasting pipeline comprising a model according to the target variable time-series data and the exogenous variable time-series data, evaluating the regular forecasting pipeline and the exogenous forecasting pipeline, selecting a pipeline according to the evaluation, and providing the selected pipeline.