A survey on graph neural networks, machine learning and deep learning techniques for time series applications in industry
Muhammad Jamal Ahmed, Alberto Mozó, Amit Karamchandani · PeerJ Computer Science · 2025
Extensive studies have been conducted to investigate Artificial Intelligence (AI) in the context of time series data. In this article, we investigate the complex domain of industrial time series, from the dimensions of classical machine learning (ML), deep neural networks (DNNs) and graph neural networks (GNNs). Current surveys often focus on a specific methodology or oversee the connection of diverse approaches; our article bridges this gap by providing an all-inclusive interpretation across numerous techniques. In addition, the aim of this article is to focus on the core areas of time series such as forecasting, classification, and anomaly detection. From traditional methodologies like Autoregressive Integrated Moving Average (ARIMA) and support vector machine (SVM) methods, the advancements of DNNs, for instance long-short-term memory (LSTMs), convolutional neural networks (CNNs), attention mechanisms, and transformers, describe how temporal information is used for forecasting, anomaly detection, and classification. Then the article discusses the advances and limitations in ML, DNN, and GNN in order to improve the different methods in either category. Lastly, we outline future directions and open research questions with the different methodologies used in time series.