Time series forecasting for multidimensional telemetry data using GAN and BiLSTM in a Digital Twin

TL;DR

This paper proposes a novel approach combining GANs and BiLSTM networks to improve multivariate time series forecasting in digital twins, addressing limitations of existing models and enhancing behavior prediction accuracy.

Contribution

It introduces an integrated GAN and BiLSTM model specifically designed for multivariate time series forecasting in digital twin applications, filling gaps in current methods.

Findings

Enhanced forecasting accuracy for multivariate data

Improved behavior prediction in digital twins

Addresses limitations of traditional models

Abstract

The research related to digital twins has been increasing in recent years. Besides the mirroring of the physical word into the digital, there is the need of providing services related to the data collected and transferred to the virtual world. One of these services is the forecasting of physical part future behavior, that could lead to applications, like preventing harmful events or designing improvements to get better performance. One strategy used to predict any system operation it is the use of time series models like ARIMA or LSTM, and improvements were implemented using these algorithms. Recently, deep learning techniques based on generative models such as Generative Adversarial Networks (GANs) have been proposed to create time series and the use of LSTM has gained more relevance in time series forecasting, but both have limitations that restrict the forecasting results. Another issue found in the literature is the challenge of handling multivariate environments/applications in time series generation. Therefore, new methods need to be studied in order to fill these gaps and, consequently, provide better resources for creating useful digital twins. In this proposal, it is going to be studied the integration of a BiLSTM layer with a time series obtained by GAN in order to improve the forecasting of all the features provided by the dataset in terms of accuracy and, consequently, improving behaviour prediction.