Modified DEMATEL Method Based on Objective Data Grey Relational Analysis for Time Series

Smart data selection can quickly sieve valuable information from initial data. Doing so improves the efficiency of analyzing situations to aid in better decision-making. Past methods have mostly been based on expert experience, which may be subjective and inefficient when dealing with large, complex datasets. Recently, the system analysis method has been exploited to find the key data. However, few studies address the indirect effects and heterogeneity of time series data. In this study, a data selection method, the modified Decision-Making Trial and Evaluation Laboratory (DEMATEL) method based on the objective data grey relational analysis (GRA), is used to enhance the ability to analyze time-series data. GRA was first applied to assess the direct impact in the raw data indicators. Then, a modified DEMATEL was adopted to find the overall impact by including the indirect impact and data heterogeneity. We applied the method to analyze the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and perform the remaining useful life (RUL) prediction of aircraft engines. The results suggest that our method predicts well. Our work offers a nuanced approach of identifying key information in time series data and has potential applications.

Automated summary

Smart data selection improves decision-making efficiency by quickly identifying valuable information from initial data. This study introduces a modified Decision-Making Trial and Evaluation Laboratory (DEMATEL) method based on objective data grey relational analysis (GRA) to enhance the analysis of time-series data. The results of applying this method to predict the remaining useful life (RUL) of aircraft engines indicate its accuracy and potential applications.