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Lorenz-attractor dataset. Computed with ˙ X = s(Y − X ); ˙ Y = rX −Y − XZ ; ˙ Z = XY −bZ and parameters used s = 10, r = 28 and b = 2.667. Color and marker size indicate amount of curvature on a logarithmic scale for better visibility.
Source publication
This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become increasingly important due to growing availability in many industrial fields. Labeling, sorting or filtering highly transient measureme...
Context in source publication
Context 1
... parameters used s = 10, r = 28 and b = 2.667 (see Figure 2). ''In these equations X is proportional to the intensity of the convective motion, while Y is proportional to the temperature difference between the ascending and descending currents, similar signs of X and Y denoting that warm fluid is rising, and cold fluid is descending.'' ...
Citations
... The strategy involves utilizing the distance function (such as Euclidean distance for univariate data) to calculate the error value of each sliding window, and identifying abnormal windows based on a provided threshold. However, determining an appropriate value for the number of clusters k is challenging [71] [72]. Clustering algorithms like Kmeans and hierarchical clustering often yield ineffective results for time series data, causing unresolved issues. ...
Industries are generating massive amounts of data due to increased automation and interconnectedness. As data from various sources becomes more available, the extraction of relevant information becomes crucial for understanding complex systems’ behavior and performance. The growing volume and complexity of time-series data in diverse industries have created a demand for effective anomaly detection methods. Detecting anomalies in multivariate time-series data presents unique challenges, such as the presence of multiple correlated variables and intricate relationships among them. Traditional approaches often fall short in detecting anomalies, making deep learning methods a promising solution. This review article provides a comprehensive analysis of different deep learning techniques for anomaly detection in time-series data, examining their applicability across various industries and discussing the associated challenges. The article emphasizes the significance of deep learning in detecting anomalies and offers valuable insights to inform decision-making processes. Furthermore, it proposes recommendations for model developers, advocating for the development of hybrid models that combine different deep learning techniques and the exploration of attention mechanisms in Recurrent Neural Networks (RNNs). These recommendations aim to overcome the challenges associated with deep learning-based anomaly detection in multivariate time-series data.
... We implement the comparative method using the same network structure and parameters as the proposed method. • RNN+Kmeans [42]. This method is similar to CAEs+Kmeans, but it replaces the TempCNN network with an RNN. ...
With the advancement of remote sensing satellite technology, the acquisition of Satellite Image Time Series (SITS) data has significantly increased, providing new opportunities and challenges for land cover analysis. Traditional unsupervised clustering methods often struggle with the complexity of these data due to limitations in scalability and generalization capabilities. In response, this paper proposes a new unsupervised learning approach called Deep Temporal Joint Clustering (DTJC) designed for efficient pixel-wise clustering of SITS data. DTJC optimizes the reconstruction of temporal information along with clustering objectives, which not only preserves the temporal dynamics of the original data but also creates a feature space conducive to clustering. Experimental results show that DTJC achieves optimal clustering performance across four publicly available multi-spectral SITS datasets, including TimeSen2Crop, Cerrado Biome, Reunion Island, and Imperial datasets. Compared to traditional K-means and projection algorithms, DTJC significantly improves clustering accuracy, especially in environments with complex geographical distributions. Leveraging the principles of the K-means clustering algorithm, DTJC showcases remarkable performance improvements over traditional optimized K-means and projection algorithms in land cover analysis, heralding a new era in the unsupervised learning landscape of SITS data. The DTJC method greatly enhances the efficiency of SITS data analysis without the need for labeled data, making it a powerful tool for automated land cover classification and environmental monitoring.
