Enterprise AI Analysis
Automated Data Wrangling for Time-Series Data
AutoDW-TS is an end-to-end framework leveraging LLMs to streamline the complex, manual process of preparing time-series data, enhancing both efficiency and predictive accuracy in machine learning applications.
Key Impact Areas
AutoDW-TS revolutionizes time-series data preparation, delivering tangible improvements in efficiency and forecasting accuracy.
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Time-Series Benchmarks Evaluated
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Avg. RMSE Improvement
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SapientML Wins
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Prophet Wins
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Solution Overview
Table Merge
Prediction Engineering & FTI
Data Cleansing & Imputation
Data Enrichment
Industry Deployment
AutoDW-TS is a comprehensive framework that processes raw tabular datasets, applies prediction engineering, infers feature types (FTI), and recommends data wrangling options like cleansing, imputation, and enrichment. The wrangled data is then ready for downstream time-series forecasting applications.
Time-series data often comes in multiple files. AutoDW-TS automatically consolidates these into a single comprehensive table through a 6-step process including Master Table Determination, Joinable Column Discovery (using Shannon Entropy), Merge Type Classification (Unionable or Column-Joinable), Unionable Merge, and Join Order determination for Column-Joinable tables, incorporating LLM-recommended aggregation functions for one-to-many relationships.
The framework automatically recommends ML task configurations, identifying ID, target, time, and series columns, along with forecast frequency using LLMs. The Feature Type Inference (FTI) module predicts feature types for each column (e.g., Numerical, Categorical, Datetime) and suggests appropriate enrichment methods, with extensions for time-series specific features like lagged features and rolling window aggregations.
Effective data cleansing addresses errors, inconsistencies, and irrelevant entries, with specialized procedures for datetime, list, and boolean fields, handling infinite values, and enforcing type uniformity. For missing values, AutoDW-TS employs an LLM-based approach to recommend the most suitable imputation method (e.g., forward fill, interpolation, seasonality-based) for each column based on its analytical context and detected patterns.
AutoDW-TS generates new features to improve ML model accuracy. This includes transforming existing features based on their inferred type (e.g., extracting domain/path from URLs) and leveraging external data from Web APIs (e.g., Holiday, Weather, Geocoding) to add contextual information. A feature selection step using Random Forest prunes less relevant enriched features.
AutoDW-TS is deployed through an interactive WebApp for user-friendly GUI interaction, Web APIs for system integration and parallel processing, and an AI agent for broader automation ecosystems. The system supports large files via Azure Blob Storage and addresses data privacy concerns through on-premise deployment options and techniques like anonymization.
AutoDW-TS End-to-End Workflow
From raw datasets to wrangled output, AutoDW-TS automates the entire preparation pipeline for time-series data.
Input (Raw Datasets)
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Table Merge
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Prediction Engineering
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FTI (Feature Type Inference)
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Data Cleansing
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Data Imputation
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Data Enrichment
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Output (Wrangled Dataset)
13.88%
Average RMSE Improvement in Forecasting Accuracy
AutoDW-TS significantly enhances forecasting performance, demonstrating its effectiveness and potential to transform time-series data preparation at scale.
| Metric | Without AutoDW-TS | With AutoDW-TS |
|---|---|---|
| SapientML Wins | 8 | 26 |
| Prophet Wins | 4 | 15 |
| Average RMSE Improvement | N/A | 13.88% |
Real-World Deployment & Scalability
Industry: Enterprise AI
Challenge: Manual data wrangling is time-consuming and error-prone, limiting productivity and scalability for time-series forecasting.
Solution: AutoDW-TS was deployed as a WebApp, Web APIs, and an AI agent, integrating with Azure services. It automates table merging, prediction engineering, cleansing, imputation, and enrichment using LLMs.
Result: The system proved practical, scalable, and versatile in real-world environments, significantly enhancing forecasting performance and supporting diverse usage scenarios for both public and enterprise users.
Calculate Your Potential ROI with AutoDW-TS
Estimate the cost savings and reclaimed hours by automating your data wrangling processes.
Annual Savings
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Hours Reclaimed
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Your Path to Automated Data Wrangling
A structured approach to integrating AutoDW-TS into your enterprise workflow.
Data Ingestion & Merge
Consolidate diverse raw datasets and identify optimal join strategies.
Intelligent Prediction Engineering
Automate ML task configuration, including ID, target, time, and series column identification.
Advanced Data Cleansing
Correct errors, handle inconsistencies, and standardize formats across all data types.
LLM-Powered Imputation
Fill missing values using context-aware, LLM-recommended methods, including seasonality.
Contextual Data Enrichment
Generate new features from existing data and integrate external knowledge via APIs.
Deployment & Integration
Seamlessly integrate AutoDW-TS into existing pipelines via WebApp, APIs, or AI Agents.
Transform Your Data Workflow
Schedule a consultation to explore how AutoDW-TS can revolutionize your time-series data preparation.
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