Aligning cause-effect pairs by their estimated Granger lag improves channel-dependent forecasting accuracy and transfer learning on synthetic time-series data.
Automated Knowledge Graph Learning in Industrial Processes
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Industrial processes generate vast amounts of time series data, yet extracting meaningful relationships and insights remains challenging. This paper introduces a framework for automated knowledge graph learning from time series data, specifically tailored for industrial applications. Our framework addresses the complexities inherent in industrial datasets, transforming them into knowledge graphs that improve decision-making, process optimization, and knowledge discovery. Additionally, it employs Granger causality to identify key attributes that can inform the design of predictive models. To illustrate the practical utility of our approach, we also present a motivating use case demonstrating the benefits of our framework in a real-world industrial scenario. Further, we demonstrate how the automated conversion of time series data into knowledge graphs can identify causal influences or dependencies between important process parameters.
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Causal Time-Series Synchronization for Multi-Dimensional Forecasting
Aligning cause-effect pairs by their estimated Granger lag improves channel-dependent forecasting accuracy and transfer learning on synthetic time-series data.