Discovering long term dependencies in noisy time series data using deep learning

TL;DR

This paper introduces a framework that captures and explains long-term dependencies in noisy time series data using deep neural networks, aiding interpretability in complex manufacturing processes.

Contribution

It presents a novel framework for understanding temporal dependencies in noisy time series data with deep learning, enhancing interpretability and decision-making.

Findings

Framework effectively captures long-term dependencies.

Applicable to synthetic and real-world datasets.

Improves interpretability of neural network decisions.

Abstract

Time series modelling is essential for solving tasks such as predictive maintenance, quality control and optimisation. Deep learning is widely used for solving such problems. When managing complex manufacturing process with neural networks, engineers need to know why machine learning model made specific decision and what are possible outcomes of following model recommendation. In this paper we develop framework for capturing and explaining temporal dependencies in time series data using deep neural networks and test it on various synthetic and real world datasets.