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U-TSS: a novel time series segmentation model based U-net applied to automatic detection of interference events in geomagnetic field data

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PEERJ COMPUTER SCIENCE 2025年 11卷 e2678-e2678页

作者： Shan, Weifeng Wang, Mengyu Xia, Jinzhu Chen, Jun Li, Qi Xing, Lili Zhang, Ruilei Wang, Maofa Zhang, Suqin Zhang, Xiuxia Inst Disaster Prevent Sch Emergency Management Sanhe Peoples R China Hebei Key Lab Resource & Environm Disaster Mech & Sanhe Hebei Peoples R China Earthquake Adm Anhui Prov Hefei Peoples R China China Earthquake Adm Inst Geophys Beijing Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin Peoples R China Earthquake Adm Jiangsu Prov Nanjing Peoples R China

With the rapid advancement of Internet of Things (IoT) technology, the volume of sensor data collection has increased significantly. These data are typically presented in the form of time series, gradually becoming a crucial component of big data. Traditional time series analysis methods struggle with complex patterns and long-term dependencies, whereas deep learning technologies offer new solutions. This study introduces the U-TSS, a U-net-based sequence-to-sequence fully convolutional network, specifically designed for one-dimensional time series segmentation tasks. U-TSS maps input sequences of arbitrary length to corresponding sequences of class labels across different temporal scales. This is achieved by implicitly classifying each individual time point in the input time series and then aggregating these classifications over varying intervals to form the final prediction. This enables precise segmentation at each time step, ensuring both global sequence awareness and accurate classification of complex time series data. We applied U-TSS to geomagnetic field observation data for the detection of high-voltage direct current (HVDC) interference events. In experiments, U-TSS achieved superior performance in detecting HVDC interference events, with accuracies of 99.42%, 94.61%, and 95.54% on the training, validation, and test sets, respectively, outperforming state-of-the-art models in accuracy, precision, recall, F1-score, and AUC. Our code can be accessed openly in the GitHub repository at https://***/wangmengyu1/U-TSS.

关键词： time series segmentation U-net Artificial intelligence Geomagnetic field observation data High-voltage direct current interference event

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time series segmentation for state-model generation of autonomous aquatic drones: A systematic framework

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年第0期90卷 103499-000页

作者： Castellini, Alberto Bicego, Manuele Masillo, Francesco Zuccotto, Maddalena Farinelli, Alessandro Univ Verona Dept Comp Sci Verona Italy

Autonomous surface vessels are becoming increasingly important for water monitoring. Their aim is to navigate rivers and lakes with limited intervention of human operators, to collect real-time data about water parameters. To reach this goal, these intelligent systems must interact with the environment and act according to the situations they face. In this work we propose a framework based on the integration of recent time-series clustering/segmentation methods and cluster validity indices, for detecting, modeling and evaluating aquatic drone states. The approach is completely data-driven and unsupervised. It takes unlabeled multivariate time series of sensor traces and returns both a set of statistically significant state-models (generated by different mathematical approaches) and a related segmentation of the dataset. We test the approach on a real dataset containing data of six campaigns, two in rivers and four in lakes, in different countries for about 5.6 h of navigation. Results show that the methodology is able to recognize known states and to discover unknown states, enabling novelty detection. The approach is therefore an easy-to-use tool for discovering and interpreting significant states in sensor data, that enables improved data analysis and drone autonomy.

关键词： time series segmentation Situation assessment State-model generation Autonomous surface vessels Activity recognition Water monitoring Model interpretation/explanation Sensor data analysis

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time series segmentation of Paleoclimate Tipping Points by an Evolutionary Algorithm

Time Series Segmentation of Paleoclimate Tipping Points by a...

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9th International Conference on Hybrid Artificial Intelligence Systems (HAIS)

作者： Perez-Ortiz, M. Gutierrez, P. A. Sanchez-Monedero, J. Hervas-Martinez, C. Nikolaou, Athanasia Dicaire, Isabelle Fernandez-Navarro, Francisco Univ Cordoba Dept Comp Sci & Numer Anal Rabanales CampusAlbert Einstein Bldg E-14071 Cordoba Spain European Space Agcy ESTEC Noordwijk Netherlands

ISBN: (纸本)9783319076171;9783319076164

Recent studies propose that some dynamical systems, such as climate, ecological and financial systems, among others, present critical transition points named to as tipping points (TP). Climate TPs can severely affect millions of lives on Earth so that an active scientific community is working on finding early warning signals. This paper deals with the segmentation of a paleoclimate time series to find segments sharing common patterns with the purpose of finding one or more kinds of segments corresponding to TPs. Due to the limitations of classical statistical methods, we propose the use of a genetic algorithm to automatically segment the series together with a method to perform time series segmentation comparisons. Without a priori information, the method clusters together most of the TPs and avoids false positives, which is a promising result given the challenging nature of the problem.

关键词： time series segmentation genetic algorithms clustering paleoclimate data tipping points abrupt climate change

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time series segmentation for Driving Scenario Detection with Fully Convolutional Networks 7

Time Series Segmentation for Driving Scenario Detection with...

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7th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS)

作者： Elspas, Philip Klose, Yannick Isele, Simon Bach, Johannes Sax, Eric Dr Ing Hc F Porsche AG Weissach Germany FZI Res Ctr Informat Technol Karlsruhe Germany

ISBN: (纸本)9789897585135

Leveraging measurement data for Advanced Driver Assistant Systems and Automated Driving Systems requires reliable meta information about covered driving scenarios. With domain expertise, rule-based detectors can be a scalable way to detect scenarios in large amounts of recorded data. However, rules might struggle with noisy data, large number of variations or corner cases and might miss valuable scenarios of interest. Finding missing scenarios manually is challenging and hardly scalable. Therefore we suggest to complement rule-based scenario detection with a data-driven approach. In this work rule-based detections are used as labels to train Fully Convolutional Networks (RCN) in a weakly supervised setup. Experiments show, that FCNs generalize well and identify additional scenarios of interest. The main contribution of this paper is twofold: First, the scenario detection is formulated as a time series segmentation problem and the capability to learn a meaningful scenario detection is demonstrated. Secondly. we show how the disagreement between the rule-based method and the learned detection method can be analyzed to find wrong or missing detections. We conclude, that the FCNs provide a scalable way to assess the quality of a rule based scenario detection without the need of large amounts of ground truth infromation.

关键词： Scenario Detection Data-Driven Development time series segmentation Fully Convolutional Networks Driving Scenarios

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time series segmentation AS A DISCOVERY TOOL A Case Study of the US and Japanese Financial Markets

TIME SERIES SEGMENTATION AS A DISCOVERY TOOL <i>A Case Study...

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International Conference on Knowledge Discovery and Information Retrieval

作者： Wong, Jian Cheng Lee, Gladys Hui Ting Zhang, Yiting Yim, Woei Shyr Fornia, Robert Paulo Xu, Danny Yuan Kok, Jun Liang Cheong, Siew Ann Nanyang Technol Univ Sch Phys & Math Sci Div Math Sci 21 Nanyang Link Singapore 637371 Singapore Univ Colorado Boulder CO 80309 USA Bard Coll Annandale On Hudson NY 12504 USA Nanyang Technol Univ Sch Phys & Math Sci Div Phys & Appl Phys Singapore 637371 Singapore

ISBN: (纸本)9789898425799

In this paper we explain how the dynamics of a complex system can be understood in terms of the low dimensional manifolds (phases), described by slowly varying effective variables, it settles onto. We then explain how we can discover these phases by grouping the large number of microscopic time series or time series segments, based on their statistical similarities, into the a small number of time series classes, each representing a distinct phase. We describe a specific recursive scheme for time series segmentation based on the Jensen-Shannon divergence, and check its performance against artificial time series data. We then apply the method on the high-frequency time series data of various US and Japanese financial market indices, where we found that the time series segments can be very naturally grouped into four to six classes, corresponding roughly with economic growth, economic crisis, market correction, and market crash. From a single time series, we can estimate the lifetimes of these macroeconomic phases, and also identify potential triggers for each phase transition. From a cross section of time series, we can further estimate the transition times, and also arrive at an unbiased and detailed picture of how financial markets react to internal or external stimuli.

关键词： time series segmentation Coarse graining Macroeconomic cycle Financial markets

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A hybrid dynamic exploitation barebones particle swarm optimisation algorithm for time series segmentation

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NEUROCOMPUTING 2019年 353卷 45-55页

作者： Duran-Rosal, Antonio M. Gutierrez, Pedro A. Carmona-Poyato, Angel Heryas-Martinez, Cesar Univ Cordoba Dept Comp Sci & Numer Anal Rabanales CampusAlbert Einstein Bldg 3rd Floor E-14071 Cordoba Spain

Large time series are difficult to be mined and preprocessed, hence reducing their number of points with minimum information loss is an active field of study. This paper proposes new methods based on time series segmentation, including the adaptation of the particle swarm optimisation algorithm (PSO) to this problem, and more advanced PSO versions, such as barebones PSO (BBPSO) and its exploitation version (BBePSO). Moreover, a novel algorithm is derived, referred to as dynamic exploitation barebones PSO (DBBePSO), which updates the importance of the social and cognitive components throughout the generations. All these algorithms are further improved by considering a final local search step based on the combination of two well-known standard segmentation algorithms (Bottom-Up and Top-Down). The performance of the different methods is evaluated using 15 time series from various application fields, and the results show that the novel algorithm (DBBePSO) and its hybrid version (HDBBePSO) outperform the rest of segmentation techniques. (C) 2019 Elsevier B.V. All rights reserved.

关键词： time series size reduction time series segmentation Particle swarm optimisation Hybrid algorithm

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An evolutionary approach to pattern-based time series segmentation

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2004年第5期8卷 471-489页

作者： Chung, FL Fu, TC Ng, V Luk, RWP Hong Kong Polytech Univ Dept Comp Kowloon Hong Kong Peoples R China

time series data, due to their numerical and continuous nature, are difficult to process, analyze, and mine. However, these tasks become easier when the data can be transformed into meaningful symbols. Most recent works on time series only address how to identify a given pattern from a time series and do not consider the problem of identifying a suitable set of time points for segmenting the time series in accordance with a given set of pattern templates (e.g., a set of technical patterns for stock analysis). However, the use of fixed-length segmentation is an oversimplified approach to this problem;hence, a dynamic approach (with high controllability) is preferable so that the time series can be segmented flexibly and effectively according to the needs of the users and the applications. In view of the fact that this segmentation problem is an optimization problem and evolutionary computation is an appropriate tool to solve it, we propose an evolutionary time series segmentation algorithm. This approach allows a sizeable set of pattern templates to be generated for mining or query. In addition, defining similarity between time series (or time series segments) is of fundamental importance in fitness computation. By identifying the perceptually important points directly from the time domain, time series segments and templates of different lengths can be compared and intuitive pattern matching can be carried out in an effective and efficient manner. Encouraging experimental results are reported from tests that segment both artificial time series generated from the combinations of pattern templates and the time series of selected Hong Kong stocks.

关键词： data mining evolutionary algorithms stock time series time series segmentation

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Simultaneous optimisation of clustering quality and approximation error for time series segmentation

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INFORMATION SCIENCES 2018年 442卷 186-201页

作者： Manuel Duran-Rosal, Antonio Antonio Gutierrez, Pedro Jose Martinez-Estudillo, Francisco Hervas-Martinez, Cesar Univ Cordoba Dept Comp Sci & Numer Anal Rabanales CampusAlbert Einstein Bldg E-14071 Cordoba Spain Univ Loyola Andalucia Dept Quantitat Methods Escritor Castilla Aguayo 4 Cordoba 14004 Spain

time series segmentation is aimed at representing a time series by using a set of segments. Some researchers perform segmentation by approximating each segment with a simple model (e.g. a linear interpolation), while others focus their efforts on obtaining homogeneous groups of segments, so that common patterns or behaviours can be detected. The main hypothesis of this paper is that both objectives are conflicting, so time series segmentation is proposed to be tackled from a multiobjective perspective, where both objectives are simultaneously considered, and the expert can choose the desired solution from a Pareto Front of different segmentations. A specific multiobjective evolutionary algorithm is designed for the purpose of deciding the cut points of the segments, integrating a clustering algorithm for fitness evaluation. The experimental validation of the methodology includes three synthetic time series and three time series from real-world problems. Nine clustering quality assessment metrics are experimentally compared to decide the most suitable one for the algorithm. The proposed algorithm shows good performance for both clustering quality and reconstruction error, improving the results of other mono-objective alternatives of the state-of-the-art and showing better results than a simple weighted linear combination of both corresponding fitness functions. (C) 2018 Elsevier Inc. All rights reserved.

关键词： time series segmentation Multiobjective optimisation Clustering Evolutionary computation

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An adaptive time series segmentation algorithm based on visibility graph and particle swarm optimization

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2024年 636卷

作者： He, Zhipeng Zhang, Shuguang Hu, Jun Dai, Fei Univ Sci & Technol China Sch Management Dept Stat & Finance Hefei 230026 Peoples R China Fuzhou Univ Sch Econ & Management Fuzhou 350108 Peoples R China Agr Bank China Cangzhou branch Cangzhou 061000 Peoples R China

time series segmentation is a crucial area of research in time series analysis as it can reveal meaningful patterns or segments hidden within time series data. In this paper, we present an accurate and efficient time series segmentation method that combines the visibility graph method, particle swarm optimization, and community detection algorithm. We start by applying visibility graph theory to process time series data, resulting in a corresponding complex network. Next, we introduce an adaptive particle swarm optimization algorithm with modularity Q as the objective function to optimize community detection. Finally, mapping the communities back to the nodes of the time series yields the segmented sequence. Our proposed method offers high segmentation accuracy and low time complexity (O(n(2))). Experimental results demonstrate that our approach outperforms existing methods in terms of segmentation accuracy on two different synthetic datasets. Furthermore, when applied to the S&P500 index dataset, it accurately identifies financial cycles and key financial events.

关键词： time series segmentation Visibility graph Particle swarm optimization Community detection S&P500 index

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Explainable time series Tree: An Explainable Top-Down time series segmentation Framework

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IEEE ACCESS 2023年 11卷 120845-120856页

作者： Silva, Vitor de Castro Zarpelao, Bruno Bogaz Medvet, Eric Barbon Jr, Sylvio Univ Estadual Londrina Dept Comp Sci BR-86057970 Londrina PR Brazil Univ Trieste Dept Engn & Architecture I-34100 Trieste Italy

A wide range of Machine Learning algorithms can model time series to address classification, forecasting, and clustering problems. However, time series may exhibit characteristics that complicate these tasks, such as repeating patterns and seasonal variations. time series segmentation could be a solution to these problems, but current approaches need to be improved. Most of them employ linear regression to solve problems such as detecting changes in a series' behaviour, bypassing tools specifically designed for these challenges, such as change detectors. Moreover, explainability is seldom taken into account during time series segmentation. To automatically identify different time series patterns using appropriate techniques while leveraging explainability, we proposed the eXplainable time series Tree (XTSTree). XTSTree divides a time series into a binary tree, hierarchically splitting it according to a criterion based on change detectors, ideally finding a cutting point that creates the two most different sub-series. The segmentation process continues until it reaches a stopping condition, which relies on a stationarity test that assesses whether the series has a sufficiently homogeneous behaviour. Based on well-behaved segments, XTSTree paves the way for a more comprehensive pattern explanation and also supports the application of explainable approaches. Our experimental study applied XTSTree on several real-life time series to isolate the series' different behaviours. To evaluate the effectiveness of our method, we used Symbolic Regression to find the best representation of the time series and its splits using algebraic expressions, comparing the differences before and after XTSTree. We show an improvement in terms of expression complexity, improving the model accuracy compared to the original time series.

关键词： Concept drift time series meta-learning time series segmentation symbolic regression

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