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LLMs as Master Forgers: Generating Synthetic Time Series Data for Manufacturing
Mantek Singh, Jeshwanth Challagundla, Prateek Karnal, Gagan Ganapathy, Vineet Shah, Ridam Arora
TL;DR
LSTM networks can be challenging to train because they require substantial data and careful hyperparameter tuning. The framework fine-tunes LLMs on manufacturing process information and uses RAG and validation to generate realistic, diverse synthetic time series. The LLM-based framework outperforms ARIMA and LSTM baselines across evaluated metrics and yields a 12% F1-score improvement over training on real data alone.
Problem
LSTM networks can be challenging to train because they require substantial data and careful hyperparameter tuning.
Method
The framework fine-tunes LLMs on manufacturing process information and uses RAG and validation to generate realistic, diverse synthetic time series.
Results
The LLM-based framework outperforms ARIMA and LSTM baselines across evaluated metrics and yields a 12% F1-score improvement over training on real data alone.
Takeaways & Limitations
Synthetic data from the framework improves downstream anomaly detection and captures statistical properties and temporal dependencies of real manufacturing data.
Takeaways & Limitations
Fine-tuning and querying large LLMs can be computationally expensive, while available data may not cover all manufacturing scenarios.
Abstract
from arXiv · showhide
This paper presents a novel framework leveraging Large Language Models (LLMs) to generate synthetic time series data for manufacturing processes. Motivated by the scarcity of labeled time-series data in real-world manufacturing settings, which hinders the development of robust machine learning models, we explore the potential of LLMs to learn complex temporal dependencies and generate realistic synthetic data. Our approach involves fine-tuning pre-trained LLMs on manufacturing process instructions and employing a Retrieval Augmented Generation (RAG) technique to enhance data diversity and realism. We evaluate our method against traditional time series modeling techniques like ARIMA and LSTMs, using quantitative metrics, PCA analysis, and downstream task performance (anomaly detection). Results demonstrate that our LLM-driven framework outperforms these baselines, generating high-quality synthetic time series data that effectively captures temporal dependencies and statistical properties of real manufacturing data, leading to improvements in downstream task performance.
I. INTRODUCTION
Manufacturing time-series data is valuable but difficult to collect at scale, motivating synthetic-data generation. The paper proposes an LLM-based framework combining fine-tuning, RAG, validation, and broad evaluation.
- I. INTRODUCTION: Large, labeled manufacturing time-series datasets are difficult to obtain because of cost, privacy concerns, and collection complexity.
- I. INTRODUCTION: ARIMA captures linear dependencies, while LSTMs model longer-range dependencies but require substantial data and careful tuning.
- I. INTRODUCTION: LLMs can learn relationships between manufacturing process instructions and time-series patterns to generate synthetic data reflecting real-world complexity.
- I. INTRODUCTION: The proposed framework combines LLM fine-tuning, retrieval-augmented generation, and data validation to produce realistic and diverse synthetic manufacturing time series.
- I. INTRODUCTION: The study evaluates generated data using quantitative metrics, statistical and temporal measures, PCA analysis, qualitative assessment, and anomaly detection.
- I. INTRODUCTION: LLM-based synthetic time-series generation remains relatively unexplored in manufacturing compared with established statistical and deep-learning approaches.
A. Traditional Statistical Methods
Traditional statistical and deep-learning methods generate time series by modeling temporal structure or learned representations. Their reported limitations include difficulty capturing nonlinear relationships, long-range dependencies, and the data and tuning demands of deep learning.
- A. Traditional Statistical Methods: AR, MA, ARIMA, and SARIMA model temporal patterns using past values, forecast errors, differencing, or seasonal structure.
- A. Traditional Statistical Methods: Statistical methods often struggle with complex nonlinear relationships and long-range dependencies in real-world manufacturing data.
- A. Traditional Statistical Methods: RNNs process sequential information recurrently, while LSTMs are designed to capture long-range dependencies in time series.
- A. Traditional Statistical Methods: GANs synthesize data through generator-discriminator competition, whereas VAEs generate samples from learned lower-dimensional latent representations.
- A. Traditional Statistical Methods: Deep-learning approaches often require large training datasets and careful hyperparameter tuning for optimal performance.
C. LLMs for Data Augmentation
LLMs have been used for augmentation in text, code, and image-captioning tasks, but their use for manufacturing time-series synthesis is less explored. This paper addresses that gap using domain-constrained manufacturing data.
- C. LLMs for Data Augmentation: Prior LLM augmentation applications include generating text, code, and image captions for downstream datasets.
- C. LLMs for Data Augmentation: LLM-based synthetic time-series generation for manufacturing is identified as a relatively unexplored application.
- C. LLMs for Data Augmentation: The dataset contains 337 friction materials, each represented by percentages of 60 raw materials grouped into six classes.
- C. LLMs for Data Augmentation: Viable compounds must satisfy predefined minimum and maximum composition constraints, including a 3%–27% range for class F.
- C. LLMs for Data Augmentation: Figure 1 presents the material-composition constraints used to define viable compounds.
C. Performance Data
The performance dataset links friction-material compositions and braking-test conditions to multivariate time-series outcomes. Its coverage is representative of common braking variations but excludes every possible manufacturing scenario.
- C. Performance Data: Each friction material has 124 braking tests, with each test producing 31 time steps of performance-related time-series data.
- C. Performance Data: Recorded variables include braking-system pressure, temperature, vehicle speed, and coefficient of friction.
- C. Performance Data: The dataset captures complex relationships among material composition, braking conditions, and resulting time-series performance.
- C. Performance Data: Study coverage focuses on material composition and standard braking parameters, excluding scenarios such as extreme weather and manufacturing defects.
- C. Performance Data: The framework is organized into three key modules for generating synthetic data, including an LLM-based generation component and a validator module.
A. Material Selection Module
The framework uses LLMs to propose constrained material compositions and generate synthetic braking time series, combining fine-tuning with retrieval of similar historical cases.
- Material proposal: GPT-3.5 Turbo proposes material compositions under domain-specific constraints, while a validator checks whether generated compositions meet predefined criteria.The model was fine-tuned on 337 existing friction materials and their compositions.
- Synthetic data generation: The data-generation module produces synthetic time series for a material composition and braking test type using GPT-3.5 Turbo with RAG.RAG is intended to improve realism for different material compositions and braking scenarios.
- Fine-tuning: Fine-tuning on 5% of 41,788 material–braking tuples teaches output structure and broad trends, including 31-point series for pressure, temperature, speed, and mu.The stage focuses on format and general trends such as increasing temperature and decreasing speed.
- Retrieval augmentation: RAG retrieves five nearest materials using combined 60-dimensional raw and six-dimensional category-reduced distances, then supplies their performance data as prompt context.The distance weights control emphasis on overall composition similarity versus material-category similarity.
- Framework integration: Combining fine-tuning with RAG lets the LLM learn general data structure while incorporating real-world knowledge about complex braking interactions.Retrieved examples expose the model to historical behavior under varied braking conditions.
C. Data Validator Module
The data validator filters synthetic outputs using manufacturing constraints, physical plausibility checks, parameter correlations, and expected time-series curve behavior.
- Validation scope: The validator applies domain-specific rules and trend analysis to ensure synthetic time-series data are consistent and reliable for downstream tasks.Its checks target both quality and consistency of generated outputs.
- Domain constraints: Material checks enforce composition constraints, such as requiring category B materials to comprise 1% to 30%, while also checking physical plausibility and totals summing to 100%.Additional checks may detect incompatible raw materials.
- Correlation checks: Correlation analysis checks whether generated parameters preserve relationships observed in real data, including inverse correlation between pressure and mu.This targets cross-parameter consistency rather than individual values alone.
- Curve behavior: Curve validation checks that temperature follows an expected exponential increase and speed follows a linear decrease during braking.These temporal-shape checks help filter unrealistic curves.
A. Evaluation Criteria
The evaluation assesses synthetic data through statistical similarity, temporal dependence, latent-space structure, and anomaly-detection performance under three training scenarios.
- Statistical similarity: KL divergence and Wasserstein distance compare real and synthetic distributions for pressure, temperature, and mu.These metrics evaluate statistical similarity between corresponding parameter distributions.
- Temporal dependence: Dynamic Time Warping distance assesses similarity between real and synthetic temporal patterns while allowing nonlinear time alignment.The metric focuses on temporal dependence rather than only marginal distributions.
- PCA analysis: PCA visualizes and compares latent representations to assess whether synthetic data capture the underlying structure of real data.The analysis provides a dimensionality-reduced view of real and synthetic datasets.
- Downstream task performance: The study evaluates anomaly detection using real-only, real-plus-synthetic, and synthetic-only training scenarios, with F1-score as the performance metric.The classifier identifies anomalous braking events.
B. Results and Analysis
The LLM-based framework outperforms ARIMA and LSTM baselines across quantitative metrics, closely matches real-data structure in PCA space, and improves anomaly detection performance.
- Quantitative Comparison: The LLM-based framework consistently outperforms ARIMA and LSTM baselines across all evaluated metrics.It captures statistical properties through lower KL divergence and Wasserstein distances, and temporal dependencies through lower DTW distances.
- PCA Analysis: The generated data closely resembles real data in PCA visualizations, indicating similar underlying structure and relationships.Figure 3 provides the visual comparison between provided and generated materials.
- Downstream Task Performance: A 12% F1-score improvement over training on real data alone was achieved in anomaly detection.The approach also outperformed models trained with data augmented by ARIMA- or LSTM-generated data.
- Framework and Impact: The framework combines LLM fine-tuning, RAG, and validation to generate realistic and diverse synthetic time-series data.RAG leverages relevant historical information, while the framework’s broader application scope includes predictive maintenance, anomaly detection, and process optimization.
- Limitations: Substantial computational costs remain associated with fine-tuning and querying large LLMs.The paper identifies efficient fine-tuning methods and open-source LLMs as possible mitigation strategies.
- Limitations: The framework does not fully cover all possible manufacturing scenarios and conditions.Its diversity and realism are bounded by the available dataset and the predefined RAG distance function.
VII. CONCLUSION
The paper concludes that its LLM-and-RAG framework generates realistic manufacturing time series and outperforms traditional methods, while outlining efficiency, retrieval, and application extensions.
- The LLM-driven framework outperforms traditional time-series methods while capturing temporal dependencies and statistical properties of real manufacturing data.Evidence includes quantitative metrics, PCA analysis, and improved downstream anomaly-detection performance.
- Future work will investigate efficient fine-tuning techniques and open-sourced LLMs to improve framework efficiency.
- Future work will explore more sophisticated retrieval and ranking mechanisms to further improve generated-data realism.
- The framework is intended for expansion into domains beyond manufacturing where synthetic time-series data can be beneficial.