Data-Driven Prediction and Drift Enhancement with Heterogeneous Graph Analysis

Manivannan K, Gowsika S, Geetha M, Baskar D, Franklin Jino R E, Kanimozhi S

2025

Abstract

Predictive model accuracy and dependability maintenance is critical in the quickly changing world of data-driven environments. This work, propose a new framework for drift detection and model updating that combines machine learning methods such as Long Short-Term Memory (LSTM) networks and Light Gradient Boosting Machine (LGBM) with statistical tests. We provide a complete strategy that extends to proactive model adjustment tactics, beginning with the quantitative changes in data distribution that identify drift. Our experimental approach, which was carried out on simulated datasets intended to replicate temporal variations in user behavior and market conditions that occur in real life, shows that, when compared to traditional static models, our method can greatly improve model resilience and reduce prediction error by up to 40%. The study also looks at the effects of quick model modification, highlighting the need to strike a balance between predictability and responsiveness. This paper provides a strong methodology for controlling idea drift and guaranteeing sustained model accuracy in dynamic contexts, adding to the body of knowledge in predictive analytics. An improved model for forecasting concept drift in sensor data is presented in this work, which is essential for preserving data quality in dynamic contexts. By combining machine learning with ARIMA, our model provides accurate drift prediction and detection. Robust performance is ensured by drift detection, prediction, and preprocessing modules as well as a feedback mechanism. When compared to conventional models, our approach exhibits better accuracy and early identification. In addition to helping with preventive maintenance scheduling and cutting costs and downtime, it promises benefits for industries that depend on accurate sensor data.

Paper Citation

in Harvard Style

K M., S G., M G., D B., R E F. and S K. (2025). Data-Driven Prediction and Drift Enhancement with Heterogeneous Graph Analysis. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 229-237. DOI: 10.5220/0013589900004664

in Bibtex Style

@conference{incoft25,
author={Manivannan K and Gowsika S and Geetha M and Baskar D and Franklin Jino R E and Kanimozhi S},
title={Data-Driven Prediction and Drift Enhancement with Heterogeneous Graph Analysis},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={229-237},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013589900004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Data-Driven Prediction and Drift Enhancement with Heterogeneous Graph Analysis
SN - 978-989-758-763-4
AU - K M.
AU - S G.
AU - M G.
AU - D B.
AU - R E F.
AU - S K.
PY - 2025
SP - 229
EP - 237
DO - 10.5220/0013589900004664
PB - SciTePress