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Thematische Bibliographien / Time series outlier detection / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Time series outlier detection“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

MENGUTAYCI, Ümmügülsüm, and Selma Ayşe ÖZEL. "An Outlier Analysis on Multi-Dimensional and Time-Series Data." AINTELIA SCIENCE NOTES 1, no. 1 (2022): 162–68. https://doi.org/10.5281/zenodo.8071461.

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Outlier detection refers to the detection of unexpected situations in the data. Outliers are fraud, hacking, mislabeled data, or unusual behavior in the system. Therefore, it is important to determine these values. In this study, outlier detection performances of the algorithms used in outlier detection analysis on different types of data sets were calculated and compared. As a result of the study, it was seen that the algorithms showed sufficient success. The highest performance was seen in the Histogram-based outlier detection algorithm with 99 % accuracy.

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Twumasi-Ankrah, Sampson, Simon Kojo Appiah, Doris Arthur, Wilhemina Adoma Pels, Jonathan Kwaku Afriyie, and Danielson Nartey. "Comparison of outlier detection techniques in non-stationary time series data." Global Journal of Pure and Applied Sciences 27, no. 1 (2021): 55–60. http://dx.doi.org/10.4314/gjpas.v27i1.7.

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This study examined the performance of six outlier detection techniques using a non-stationary time series dataset. Two key issues were of interest. Scenario one was the method that could correctly detect the number of outliers introduced into the dataset whiles scenario two was to find the technique that would over detect the number of outliers introduced into the dataset, when a dataset contains only extreme maxima values, extreme minima values or both. Air passenger dataset was used with different outliers or extreme values ranging from 1 to 10 and 40. The six outlier detection techniques u

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Ji, Yanjie, Dounan Tang, Weihong Guo, Phil T. Blythe, and Gang Ren. "Detection of Outliers in a Time Series of Available Parking Spaces." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/416267.

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With the provision of any source of real-time information, the timeliness and accuracy of the data provided are paramount to the effectiveness and success of the system and its acceptance by the users. In order to improve the accuracy and reliability of parking guidance systems (PGSs), the technique of outlier mining has been introduced for detecting and analysing outliers in available parking space (APS) datasets. To distinguish outlier features from the APS’s overall periodic tendency, and to simultaneously identify the two types of outliers which naturally exist in APS datasets with intrins

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Choi, Jeong In, In Ok Um, and Hyung Jun Choa. "Outlier detection in time series data." Korean Journal of Applied Statistics 29, no. 5 (2016): 907–20. http://dx.doi.org/10.5351/kjas.2016.29.5.907.

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Choy, Kokyo. "Outlier detection for stationary time series." Journal of Statistical Planning and Inference 99, no. 2 (2001): 111–27. http://dx.doi.org/10.1016/s0378-3758(01)00081-7.

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Abraham, Bovas, and Alice Chuang. "Outlier Detection and Time Series Modeling." Technometrics 31, no. 2 (1989): 241–48. http://dx.doi.org/10.1080/00401706.1989.10488517.

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7

Ljung, Greta M. "On Outlier Detection in Time Series." Journal of the Royal Statistical Society: Series B (Methodological) 55, no. 2 (1993): 559–67. http://dx.doi.org/10.1111/j.2517-6161.1993.tb01924.x.

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Chung, Se Yeon, and Sang Cheol Kim. "Anomaly Detection in Livestock Environmental Time Series Data Using LSTM Autoencoders: A Comparison of Performance Based on Threshold Settings." Korean Institute of Smart Media 13, no. 4 (2024): 48–56. http://dx.doi.org/10.30693/smj.2024.13.4.48.

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In the livestock industry, detecting environmental outliers and predicting data are crucial tasks. Outliers in livestock environment data, typically gathered through time-series methods, can signal rapid changes in the environment and potential unexpected epidemics. Prompt detection and response to these outliers are essential to minimize stress in livestock and reduce economic losses for farmers by early detection of epidemic conditions. This study employs two methods to experiment and compare performances in setting thresholds that define outliers in livestock environment data outlier detect

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Nguyen, Huy Dinh, and Trong Dinh Tran. "Detecting outliers in GNSS position time series using machine learning techniques." Journal of Mining and Earth Sciences 64, no. 4 (2023): 22–30. http://dx.doi.org/10.46326/jmes.2023.64(4).03.

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The Global Navigation Satellite System (GNSS) position time series is applied in studies that require high-precision positioning, such as monitoring tectonic movements and Earth deformation. Outliers in GNSS position time series can significantly impact the accuracy of station positioning and movement parameters, leading to distorted data analysis outcomes. This study investigates the effectiveness of three machine learning techniques, including-Isolation Forest, One-Class Support Vector Machines (O-C SVM), and Local Outlier Factor (LOF) for outlier detection in GNSS position time series, with

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Lee, Jun-Whan, Sun-Cheon Park, Duk Kee Lee, and Jong Ho Lee. "Tsunami arrival time detection system applicable to discontinuous time series data with outliers." Natural Hazards and Earth System Sciences 16, no. 12 (2016): 2603–22. http://dx.doi.org/10.5194/nhess-16-2603-2016.

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Abstract. Timely detection of tsunamis with water level records is a critical but logistically challenging task because of outliers and gaps. Since tsunami detection algorithms require several hours of past data, outliers could cause false alarms, and gaps can stop the tsunami detection algorithm even after the recording is restarted. In order to avoid such false alarms and time delays, we propose the Tsunami Arrival time Detection System (TADS), which can be applied to discontinuous time series data with outliers. TADS consists of three algorithms, outlier removal, gap filling, and tsunami de

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Tran, Trong Dinh, Toan Duy Dao, Tung So Vu, et al. "Outlier detection in GNSS position time series." Science and Technology Development Journal 19, no. 2 (2016): 43–50. http://dx.doi.org/10.32508/stdj.v19i2.665.

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The continuous GNSS stations are used to determine the displacement velocities, seasonal variation, amplitude of tectonic activities,… To accurately determine these factors, the first is to remove outliers in GNSS position time series. In general, filtering approaches are subjectively selected based on the experience and visual interpretation of experts. Therefore, the process may lead to a waste of time or confusion, especially for stations with long-term continuously recorded data. The purpose of paper is to introduce the applicability of several algorithms and methods of filtering outliers

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Vorotnikov, I., A. Rozanov, M. Sidelnikova, S. Tkachev, and L. Volochuk. "Outlier Detection of the Agricultural Time Series." IOP Conference Series: Earth and Environmental Science 723, no. 4 (2021): 042070. http://dx.doi.org/10.1088/1755-1315/723/4/042070.

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Olewuezi, N. P., B. Onoghojobi, and A. O. Aduobi. "OUTLIER DETECTION IN UNIVARIATE TIME SERIES DATA." Far East Journal of Theoretical Statistics 50, no. 2 (2015): 143–51. http://dx.doi.org/10.17654/fjtsmar2015_143_151.

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Yulistiani, Selma, and Suliadi Suliadi. "Deteksi Pencilan pada Model ARIMA dengan Bayesian Information Criterion (BIC) Termodifikasi." STATISTIKA: Journal of Theoretical Statistics and Its Applications 19, no. 1 (2019): 29–37. http://dx.doi.org/10.29313/jstat.v19i1.4740.

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Time series data may be affected by special events or circumstances such as promotions, natural disasters, etc. These events can lead to inconsistent observations in the series called outliers. Because outliers can make invalid conclusions, it is important to carry out procedures in detecting outlier effects. In outlier detection there is one type of outlier, namely additive outlier (AO). The process of detecting additive outliers in the ARIMA model can be said as a model selection problem, where the candidate model assumes additive outliers at a certain time. In the selection of models there

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Blázquez-García, Ane, Angel Conde, Usue Mori, and Jose A. Lozano. "A Review on Outlier/Anomaly Detection in Time Series Data." ACM Computing Surveys 54, no. 3 (2021): 1–33. http://dx.doi.org/10.1145/3444690.

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Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on unsupervised outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that charac

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Li, Jianbo, Lecheng Zheng, Yada Zhu, and Jingrui He. "Outlier Impact Characterization for Time Series Data." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 11595–603. http://dx.doi.org/10.1609/aaai.v35i13.17379.

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For time series data, certain types of outliers are intrinsically more harmful for parameter estimation and future predictions than others, irrespective of their frequency. In this paper, for the first time, we study the characteristics of such outliers through the lens of the influence functional from robust statistics. In particular, we consider the input time series as a contaminated process, with the recurring outliers generated from an unknown contaminating process. Then we leverage the influence functional to understand the impact of the contaminating process on parameter estimation. The

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Su, Yunxiang, Shaoxu Song, Xiangdong Huang, Chen Wang, and Jianmin Wang. "Distance-Based Outlier Query Optimization in Apache IoTDB." Proceedings of the VLDB Endowment 17, no. 11 (2024): 2778–90. http://dx.doi.org/10.14778/3681954.3681962.

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While outlier detection has been widely studied over streaming data, the query of outliers in time series databases was largely overlooked. Apache IoTDB, an open-source time series database, employs LSM-tree based storage to support intensive writing workloads, yet this storage structure unfortunately encumbers the outlier query performing. In the system, data points of a time series may be stored in multiple files with overlapping time ranges, owing to the far delayed data arrivals, which are simply discarded in streaming outlier detection. Given the overlapping time ranges, it is not able to

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Li, Zhihua, Ziyuan Li, Ning Yu, and Steven Wen. "Locality-Based Visual Outlier Detection Algorithm for Time Series." Security and Communication Networks 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/1869787.

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Physiological theories indicate that the deepest impression for time series data with respect to the human visual system is its extreme value. Based on this principle, by researching the strategies of extreme-point-based hierarchy segmentation, the hierarchy-segmentation-based data extraction method for time series, and the ideas of locality outlier, a novel outlier detection model and method for time series are proposed. The presented algorithm intuitively labels an outlier factor to each subsequence in time series such that the visual outlier detection gets relatively direct. The experimenta

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Lai, Kwei-Herng, Daochen Zha, Guanchu Wang, et al. "TODS: An Automated Time Series Outlier Detection System." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 16060–62. http://dx.doi.org/10.1609/aaai.v35i18.18012.

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We present TODS, an automated Time Series Outlier Detection System for research and industrial applications. TODS is a highly modular system that supports easy pipeline construction. The basic building block of TODS is primitive, which is an implementation of a function with hyperparameters. TODS currently supports 70 primitives, including data processing, time series processing, feature analysis, detection algorithms, and a reinforcement module. Users can freely construct a pipeline using these primitives and perform end- to-end outlier detection with the constructed pipeline. TODS provides a

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Battaglia, Francesco, and Lia Orfei. "Outlier Detection And Estimation In NonLinear Time Series." Journal of Time Series Analysis 26, no. 1 (2005): 107–21. http://dx.doi.org/10.1111/j.1467-9892.2005.00392.x.

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Anto Praveena, M. D., and B. Bharathi. "A Long Short Term Memory with Peephole Connections and Generative Adversarial Network Based Collaborative Methodology to Identify Outliers in ECG Dataset." Journal of Computational and Theoretical Nanoscience 17, no. 8 (2020): 3798–803. http://dx.doi.org/10.1166/jctn.2020.9273.

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Big Data analytics has become an upward field, and it plays a pivotal role in Healthcare and research practices. Big data analytics in healthcare cover vast numbers of dynamic heterogeneous data integration and analysis. Medical records of patients include several data including medical conditions, medications and test findings. One of the major challenges of analytics and prediction in healthcare is data preprocessing. In data preprocessing the outlier identification and correction is the important challenge. Outliers are exciting values that deviates from other values of the attribute; they

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Hu, Wei, and Junpeng Bao. "The Outlier Interval Detection Algorithms on Astronautical Time Series Data." Mathematical Problems in Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/979035.

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The Outlier Interval Detection is a crucial technique to analyze spacecraft fault, locate exception, and implement intelligent fault diagnosis system. The paper proposes two OID algorithms on astronautical Time Series Data, that is, variance based OID (VOID) and FFT andknearest Neighbour based OID (FKOID). The VOID algorithm divides TSD into many intervals and measures each interval’s outlier score according to its variance. This algorithm can detect the outlier intervals with great fluctuation in the time domain. It is a simple and fast algorithm with less time complexity, but it ignores the

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Huda, Nur'ainul Miftahul, Utriweni Mukhaiyar, and Nurfitri Imro'ah. "AN ITERATIVE PROCEDURE FOR OUTLIER DETECTION IN GSTAR(1;1) MODEL." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 3 (2022): 975–84. http://dx.doi.org/10.30598/barekengvol16iss3pp975-984.

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Outliers are observations that differ significantly from others that can affect the estimation results in the model and reduce the estimator's accuracy. To deal with outliers is to remove outliers from the data. However, sometimes important information is contained in the outlier, so eliminating outliers is a misinterpretation. There are two types of outliers in the time series model, Innovative Outlier (IO) and Additive Outlier (AO). In the GSTAR model, outliers and spatial and time correlations can also be detected. We introduce an iterative procedure for detecting outliers in the GSTAR mode

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Yu, Yufeng, Yuelong Zhu, Shijin Li, and Dingsheng Wan. "Time Series Outlier Detection Based on Sliding Window Prediction." Mathematical Problems in Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/879736.

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In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI), which can be calculated by the predicted

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Serras, Jorge L., Susana Vinga, and Alexandra M. Carvalho. "Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks." Applied Sciences 11, no. 4 (2021): 1955. http://dx.doi.org/10.3390/app11041955.

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Outliers are observations suspected of not having been generated by the underlying process of the remaining data. Many applications require a way of identifying interesting or unusual patterns in multivariate time series (MTS), now ubiquitous in many applications; however, most outlier detection methods focus solely on univariate series. We propose a complete and automatic outlier detection system covering the pre-processing of MTS data that adopts a dynamic Bayesian network (DBN) modeling algorithm. The latter encodes optimal inter and intra-time slice connectivity of transition networks capa

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Silva, Maria Eduarda, Isabel Pereira, and Brendan McCabe. "Bayesian Outlier Detection in Non‐Gaussian Autoregressive Time Series." Journal of Time Series Analysis 40, no. 5 (2018): 631–48. http://dx.doi.org/10.1111/jtsa.12439.

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Galeano, Pedro, Daniel Peña, and Ruey S. Tsay. "Outlier Detection in Multivariate Time Series by Projection Pursuit." Journal of the American Statistical Association 101, no. 474 (2006): 654–69. http://dx.doi.org/10.1198/016214505000001131.

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Abuzaid, A. H., I. B. Mohamed, and A. G. Hussin. "Procedures for outlier detection in circular time series models." Environmental and Ecological Statistics 21, no. 4 (2014): 793–809. http://dx.doi.org/10.1007/s10651-014-0281-8.

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Li, Gen, and Jason J. Jung. "Dynamic graph embedding for outlier detection on multiple meteorological time series." PLOS ONE 16, no. 2 (2021): e0247119. http://dx.doi.org/10.1371/journal.pone.0247119.

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Existing dynamic graph embedding-based outlier detection methods mainly focus on the evolution of graphs and ignore the similarities among them. To overcome this limitation for the effective detection of abnormal climatic events from meteorological time series, we proposed a dynamic graph embedding model based on graph proximity, called DynGPE. Climatic events are represented as a graph where each vertex indicates meteorological data and each edge indicates a spurious relationship between two meteorological time series that are not causally related. The graph proximity is described as the dist

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Naidoo, Vashalen, and Shengzhi Du. "A Deep Learning Method for the Detection and Compensation of Outlier Events in Stock Data." Electronics 11, no. 21 (2022): 3465. http://dx.doi.org/10.3390/electronics11213465.

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The stock price is a culmination of numerous factors that are not necessarily quantifiable and significantly affected by anomalies. The forecasting accuracy of stock prices is negatively affected by these anomalies. However, very few methods are available for detecting, modelling, and compensating for anomalies in financial time series given the critical roles of better management of funds and accurate forecasting of anomalies. Time series data are a data type that changes over a defined time interval. Each value in the data set has some relation to the previous values in the series. This attr

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Tian, Jinzhao, Tianya Zhao, Zhuorui Li, Tian Li, Haipei Bie, and Vivian Loftness. "VOD: Vision-Based Building Energy Data Outlier Detection." Machine Learning and Knowledge Extraction 6, no. 2 (2024): 965–86. http://dx.doi.org/10.3390/make6020045.

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Outlier detection plays a critical role in building operation optimization and data quality maintenance. However, existing methods often struggle with the complexity and variability of building energy data, leading to poorly generalized and explainable results. To address the gap, this study introduces a novel Vision-based Outlier Detection (VOD) approach, leveraging computer vision models to spot outliers in the building energy records. The models are trained to identify outliers by analyzing the load shapes in 2D time series plots derived from the energy data. The VOD approach is tested on f

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Roos-Hoefgeest Toribio, Mario, Alejandro Garnung Menéndez, Sara Roos-Hoefgeest Toribio, and Ignacio Álvarez García. "A Novel Approach to Speed Up Hampel Filter for Outlier Detection." Sensors 25, no. 11 (2025): 3319. https://doi.org/10.3390/s25113319.

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Outlier detection is a critical task in time series analysis, essential to maintaining data quality and allowing for accurate subsequent analysis. The Hampel filter, a decision filter that replaces outliers in a data window with the median, is widely used for outlier detection in time series due to its simplicity and effectiveness. While effective, its computational complexity, primarily due to the calculation of the Median Absolute Deviation (MAD), poses limitations for large-scale and real-time applications. This paper proposes a novel Hampel filter variant that replaces the MAD with an orig

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Matsue, Kiyotaka, and Mahito Sugiyama. "Unsupervised feature extraction from multivariate time series for outlier detection." Intelligent Data Analysis 26, no. 6 (2022): 1451–67. http://dx.doi.org/10.3233/ida-216128.

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Although various feature extraction algorithms have been developed for time series data, it is still challenging to obtain a flat vector representation with incorporating both of time-wise and variable-wise association between multiple time series. Here we develop an algorithm, called Unsupervised Feature Extraction using Kernel and Stacking (UFEKS), that constructs feature vector representation for multiple time series in an unsupervised manner. UFEKS constructs a kernel matrix for the set of subsequences from each time series and horizontally concatenates all matrices. Then we can treat each

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Baragona, Roberto, and Francesco Battaglia. "Outliers Detection in Multivariate Time Series by Independent Component Analysis." Neural Computation 19, no. 7 (2007): 1962–84. http://dx.doi.org/10.1162/neco.2007.19.7.1962.

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In multivariate time series, outlying data may be often observed that do not fit the common pattern. Occurrences of outliers are unpredictable events that may severely distort the analysis of the multivariate time series. For instance, model building, seasonality assessment, and forecasting may be seriously affected by undetected outliers. The structure dependence of the multivariate time series gives rise to the well-known smearing and masking phenomena that prevent using most outliers' identification techniques. It may be noticed, however, that a convenient way for representing multiple outl

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Erz, Marcus, Jeremy Floyd Kielman, Bahar Selvi Uzun, and Gabriele Stefanie Gühring. "Anomaly detection in multidimensional time series—a graph-based approach." Journal of Physics: Complexity 2, no. 4 (2021): 045018. http://dx.doi.org/10.1088/2632-072x/ac392c.

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Abstract As the digital transformation is taking place, more and more data is being generated and collected. To generate meaningful information and knowledge researchers use various data mining techniques. In addition to classification, clustering, and forecasting, outlier or anomaly detection is one of the most important research areas in time series analysis. In this paper we present a method for detecting anomalies in multidimensional time series using a graph-based algorithm. We transform time series data to graphs prior to calculating the outlier since it offers a wide range of graph-base

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Tian, Bo, Dian Hong Wang, Fen Xiong Chen, and Zheng Pu Zhang. "Based on ETEO Pattern Abnormal Event Detection in Wireless Sensor Networks." Advanced Materials Research 926-930 (May 2014): 1886–89. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.1886.

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This paper presents a new algorithm for the detection of abnormal events in Wireless Sensor Networks (WSN). Abnormal events are sets of data points that correspond to interesting patterns in the underlying phenomenon that the network monitors. This algorithm is inspired from time-series data mining techniques and transforms a stream of sensor readings into an Extension Temporal Edge Operator (ETEO) of time series pattern representation, and then extracts the three eigenvalue of each sub-pattern, that is, patterns length, patterns slope and patterns mean to map time series to feature space, and

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Wen, Junzhi, Azim Ahmadzadeh, Manolis K. Georgoulis, Viacheslav M. Sadykov, and Rafal A. Angryk. "Outlier Detection and Removal in Multivariate Time Series for a More Robust Machine Learning–based Solar Flare Prediction." Astrophysical Journal Supplement Series 277, no. 2 (2025): 60. https://doi.org/10.3847/1538-4365/adb9e3.

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Abstract Timely and accurate prediction of solar flares is a crucial task due to the danger they pose to human life and infrastructure beyond Earth’s atmosphere. Although various machine learning algorithms have been employed to improve solar flare prediction, there has been limited focus on improving performance using outlier detection. In this study, we propose the use of a tree-based outlier detection algorithm, Isolation Forest (iForest), to identify multivariate time-series instances within the flare-forecasting benchmark data set, Space Weather Analytics for Solar Flares (SWAN-SF). By re

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Ramesh Kumar, Sowmya. "Anomaly Detection Techniques in Time Series Forecasting: Identifying Outliers." International Journal of Science and Research (IJSR) 9, no. 11 (2020): 1707–9. http://dx.doi.org/10.21275/sr24213014030.

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Plazas-Nossa, Leonardo, Miguel Antonio Ávila Angulo, and Andres Torres. "Detection of Outliers and Imputing of Missing Values for Water Quality UV-VIS Absorbance Time Series." Ingeniería 22, no. 1 (2017): 09. http://dx.doi.org/10.14483/udistrital.jour.reving.2017.1.a01.

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Context: The UV-Vis absorbance collection using online optical captors for water quality detection may yield outliers and/or missing values. Therefore, data pre-processing is a necessary pre-requisite to monitoring data processing. Thus, the aim of this study is to propose a method that detects and removes outliers as well as fills gaps in time series.Method: Outliers are detected using Winsorising procedure and the application of the Discrete Fourier Transform (DFT) and the Inverse of Fast Fourier Transform (IFFT) to complete the time series. Together, these tools were used to analyse a case

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Lestari, Lisa, Evy Sulistianingsih, and Hendra Perdana. "VECTOR AUTOREGRESSIVE WITH OUTLIER DETECTION ON RAINFALL AND WIND SPEED DATA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 1 (2024): 0117–28. http://dx.doi.org/10.30598/barekengvol18iss1pp0117-0128.

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Vector Autoregressive (VAR) is a multivariate time series model that analyzes more than one variable where each variable in the model is endogenous. VAR is one of the models used in forecasting rainfall and wind speed. In observations of rainfall and wind speed, there are usually a series of events whose values are far from other observations or can be said to be outliers. The purpose of this study is to compare the VAR model on rainfall and wind speed data before and after outlier detection. This study uses secondary data, namely monthly data on rainfall and wind speed from 2019 to 2021. From

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Campos, David, Tung Kieu, Chenjuan Guo, et al. "Unsupervised time series outlier detection with diversity-driven convolutional ensembles." Proceedings of the VLDB Endowment 15, no. 3 (2021): 611–23. http://dx.doi.org/10.14778/3494124.3494142.

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With the sweeping digitalization of societal, medical, industrial, and scientific processes, sensing technologies are being deployed that produce increasing volumes of time series data, thus fueling a plethora of new or improved applications. In this setting, outlier detection is frequently important, and while solutions based on neural networks exist, they leave room for improvement in terms of both accuracy and efficiency. With the objective of achieving such improvements, we propose a diversity-driven, convolutional ensemble. To improve accuracy, the ensemble employs multiple basic outlier

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López-Oriona, Ángel, and José A. Vilar. "Outlier detection for multivariate time series: A functional data approach." Knowledge-Based Systems 233 (December 2021): 107527. http://dx.doi.org/10.1016/j.knosys.2021.107527.

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Bui, Anh Tuan, and Chi-Hyuck Jun. "An Improved Iterative Procedure for Outlier Detection in Time Series." Journal of Korean Institute of Industrial Engineers 38, no. 1 (2012): 17–24. http://dx.doi.org/10.7232/jkiie.2012.38.1.017.

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Lu, Jun, Lei Shi, and Fei Chen. "Outlier Detection in Time Series Models Using Local Influence Method." Communications in Statistics - Theory and Methods 41, no. 12 (2012): 2202–20. http://dx.doi.org/10.1080/03610926.2011.558664.

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Su, Wei-xing, Yun-long Zhu, Fang Liu, and Kun-yuan Hu. "On-line outlier and change point detection for time series." Journal of Central South University 20, no. 1 (2013): 114–22. http://dx.doi.org/10.1007/s11771-013-1466-2.

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Kaliyaperumal, Senthamarai Kannan, Manoj Kuppusamy, and Arumugam Subbanna Gounder. "Outlier Detection and Missing Value in Time Series Ozone Data." International Journal of Scientific Research in Knowledge 3, no. 9 (2015): 220–26. http://dx.doi.org/10.12983/ijsrk-2015-p0220-0226.

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Motta, Anderson C. O., and Luiz K. Hotta. "Detection of Patches of Outliers in Stochastic Volatility Processes." São Paulo Journal of Mathematical Sciences 8, no. 2 (2014): 169. http://dx.doi.org/10.11606/issn.2316-9028.v8i2p169-191.

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Because the volatility of nancial asset returns tends to arrive in clusters, it is quite likely that outliers appear in patches. In this case, most of the statistical tests developed to detect outliers have low power. We propose to use the posterior distribution of the size of the outlier and of the probability of the presence of an outlier at each observation to detect and estimate the outlier. This sampling algorithm is an adapted version of the algorithm proposed by Justel et al. (2001) for autoregressive time-series models. Our proposed sampling procedure is applied to a simulated sample a

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Ye, Feng, Zihao Liu, Qinghua Liu, and Zhijian Wang. "Hydrologic Time Series Anomaly Detection Based on Flink." Mathematical Problems in Engineering 2020 (May 28, 2020): 1–12. http://dx.doi.org/10.1155/2020/3187697.

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The data mining and calculation of time series in critical application is still worth studying. Currently, in the field of hydrological time series, most of the detection of outliers focus on improving the specificity. To efficiently detect outliers in massive hydrologic sensor data, an anomaly detection method for hydrological time series based on Flink is proposed. Firstly, the sliding window and the ARIMA model are used to forecast data stream. Then, the confidence interval is calculated for the prediction result, and the results outside the interval range are judged as alternative anomaly

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Kyo, Koki. "Reinforcing Moving Linear Model Approach: Theoretical Assessment of Parameter Estimation and Outlier Detection." Axioms 14, no. 7 (2025): 479. https://doi.org/10.3390/axioms14070479.

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This paper reinforces the previously proposed moving linear (ML) model approach for time series analysis by introducing theoretically grounded enhancements. The ML model flexibly decomposes a time series into constrained and remaining components, enabling the extraction of trends and fluctuations with minimal structural assumptions. Building on this framework, we present two key improvements. First, we develop a theoretically justified evaluation criterion that facilitates coherent estimation of model parameters, particularly the width of the time interval. Second, we enhance the extended ML (

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Кобилін, І. О., and А. І. Ніколайчук. "MONITORING AND DIAGNOSING FAULTS IN ONLINE MODE USING TIME SERIES DATA." Системи обробки інформації, no. 3(178) (December 2, 2024): 27–32. https://doi.org/10.30748/soi.2024.178.03.

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This paper describes a technique for using time series data to monitor and diagnose faults in production systems. Predictive maintenance is the focus of research in order to assess equipment condition and anticipate potential failures. The method reduces the need for user intervention by combining Automatic Diagnostic Systems (ADS) with continuous monitoring systems to detect equipment failures early on. Three distinct techniques were tested on temperature sensor time series data: Isolation Forest (IF), Local Outlier Factor (LOF), and One-Class Support Vector Machine (OCSVM). Graphs showing ho

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