Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan.
Department of Computer Science and Information Technology, University of Chakwal, Chakwal, Pakistan.
PLoS One. 2024 Jun 6;19(6):e0303890. doi: 10.1371/journal.pone.0303890. eCollection 2024.
Anomaly detection in time series data is essential for fraud detection and intrusion monitoring applications. However, it poses challenges due to data complexity and high dimensionality. Industrial applications struggle to process high-dimensional, complex data streams in real time despite existing solutions. This study introduces deep ensemble models to improve traditional time series analysis and anomaly detection methods. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks effectively handle variable-length sequences and capture long-term relationships. Convolutional Neural Networks (CNNs) are also investigated, especially for univariate or multivariate time series forecasting. The Transformer, an architecture based on Artificial Neural Networks (ANN), has demonstrated promising results in various applications, including time series prediction and anomaly detection. Graph Neural Networks (GNNs) identify time series anomalies by capturing temporal connections and interdependencies between periods, leveraging the underlying graph structure of time series data. A novel feature selection approach is proposed to address challenges posed by high-dimensional data, improving anomaly detection by selecting different or more critical features from the data. This approach outperforms previous techniques in several aspects. Overall, this research introduces state-of-the-art algorithms for anomaly detection in time series data, offering advancements in real-time processing and decision-making across various industrial sectors.
时间序列数据中的异常检测对于欺诈检测和入侵监测应用至关重要。然而，由于数据的复杂性和高维度，这一任务具有挑战性。尽管存在现有解决方案，但工业应用在实时处理高维、复杂数据流方面仍面临困难。本研究引入了深度集成模型，以改进传统的时间序列分析和异常检测方法。递归神经网络（RNN）和长短期记忆（LSTM）网络有效地处理变长序列，并捕获长期关系。卷积神经网络（CNN）也被研究，特别是用于单变量或多变量时间序列预测。Transformer 是一种基于人工神经网络（ANN）的架构，在各种应用中，包括时间序列预测和异常检测，都取得了很有前景的结果。图神经网络（GNN）通过捕获时间序列数据的时间连接和周期之间的相互依赖关系来识别时间序列异常。提出了一种新的特征选择方法来解决高维数据带来的挑战，通过从数据中选择不同的或更关键的特征来提高异常检测的性能。该方法在多个方面都优于以前的技术。总的来说，本研究为时间序列数据中的异常检测引入了最先进的算法，为各个工业领域的实时处理和决策提供了改进。
