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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 12. Februar 2022

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1

Twumasi-Ankrah, Sampson, Simon Kojo Appiah, Doris Arthur, Wilhemina Adoma Pels, Jonathan Kwaku Afriyie und Danielson Nartey. „Comparison of outlier detection techniques in non-stationary time series data“. Global Journal of Pure and Applied Sciences 27, Nr. 1 (05.03.2021): 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 used in this study were Mahalanobis distance, depth-based, robust kernel-based outlier factor (RKOF), generalized dispersion, Kth nearest neighbors distance (KNND), and principal component (PC) methods. When detecting extreme maxima, the Mahalanobis and the principal component methods performed better in correctly detecting outliers in the dataset. Also, the Mahalanobis method could identify more outliers than the others, making it the "best" method for the extreme minima category. The kth nearest neighbor distance method was the "best" method for not over-detecting the number of outliers for extreme minima. However, the Mahalanobis distance and the principal component methods were the "best" performed methods for not over-detecting the number of outliers for the extreme maxima category. Therefore, the Mahalanobis outlier detection technique is recommended for detecting outlier in nonstationary time series data.

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Ji, Yanjie, Dounan Tang, Weihong Guo, Phil T. Blythe und 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 intrinsically distinct statistical features, a two-phase detection method is proposed whereby an improved density-based detection algorithm named “local entropy based weighted outlier detection” (EWOD) is also incorporated. Real-world data from parking facilities in the City of Newcastle upon Tyne was used to test the hypothesis. Thereafter, experimental tests were carried out for a comparative study in which the outlier detection performances of the two-phase detection method, statistic-based method, and traditional density-based method were compared and contrasted. The results showed that the proposed method can identify two different kinds of outliers simultaneously and can give a high identifying accuracy of 100% and 92.7% for the first and second types of outliers, respectively.

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Choi, Jeong In, In Ok Um und Hyung Jun Choa. „Outlier detection in time series data“. Korean Journal of Applied Statistics 29, Nr. 5 (31.08.2016): 907–20. http://dx.doi.org/10.5351/kjas.2016.29.5.907.

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4

Choy, Kokyo. „Outlier detection for stationary time series“. Journal of Statistical Planning and Inference 99, Nr. 2 (Dezember 2001): 111–27. http://dx.doi.org/10.1016/s0378-3758(01)00081-7.

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5

Abraham, Bovas, und Alice Chuang. „Outlier Detection and Time Series Modeling“. Technometrics 31, Nr. 2 (Mai 1989): 241–48. http://dx.doi.org/10.1080/00401706.1989.10488517.

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6

Ljung, Greta M. „On Outlier Detection in Time Series“. Journal of the Royal Statistical Society: Series B (Methodological) 55, Nr. 2 (Januar 1993): 559–67. http://dx.doi.org/10.1111/j.2517-6161.1993.tb01924.x.

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7

Tran, Trong Dinh, Toan Duy Dao, Tung So Vu, Dung Ngoc Luong, Chieu Dinh Vu, Son Ngoc Bui und Hang Thi Ha. „Outlier detection in GNSS position time series“. Science and Technology Development Journal 19, Nr. 2 (30.06.2016): 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 in GNSS position time series.

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Olewuezi, N. P., B. Onoghojobi und A. O. Aduobi. „OUTLIER DETECTION IN UNIVARIATE TIME SERIES DATA“. Far East Journal of Theoretical Statistics 50, Nr. 2 (09.06.2015): 143–51. http://dx.doi.org/10.17654/fjtsmar2015_143_151.

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Vorotnikov, I., A. Rozanov, M. Sidelnikova, S. Tkachev und L. Volochuk. „Outlier Detection of the Agricultural Time Series“. IOP Conference Series: Earth and Environmental Science 723, Nr. 4 (01.03.2021): 042070. http://dx.doi.org/10.1088/1755-1315/723/4/042070.

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10

Blázquez-García, Ane, Angel Conde, Usue Mori und Jose A. Lozano. „A Review on Outlier/Anomaly Detection in Time Series Data“. ACM Computing Surveys 54, Nr. 3 (Juni 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 characterize an outlier detection technique.

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Lee, Jun-Whan, Sun-Cheon Park, Duk Kee Lee und Jong Ho Lee. „Tsunami arrival time detection system applicable to discontinuous time series data with outliers“. Natural Hazards and Earth System Sciences 16, Nr. 12 (09.12.2016): 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 detection, which are designed to update whenever new data are acquired. After calibrating the thresholds and parameters for the Ulleung-do surge gauge located in the East Sea (Sea of Japan), Korea, the performance of TADS was discussed based on a 1-year dataset with historical tsunamis and synthetic tsunamis. The results show that the overall performance of TADS is effective in detecting a tsunami signal superimposed on both outliers and gaps.

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Battaglia, Francesco, und Lia Orfei. „Outlier Detection And Estimation In NonLinear Time Series“. Journal of Time Series Analysis 26, Nr. 1 (Januar 2005): 107–21. http://dx.doi.org/10.1111/j.1467-9892.2005.00392.x.

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13

Li, Zhihua, Ziyuan Li, Ning Yu und 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 experimental results demonstrate the average advantage of the developed method over the compared methods and the efficient data reduction capability for time series, which indicates the promising performance of the proposed method and its practical application value.

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Yulistiani, Selma, und Suliadi Suliadi. „Deteksi Pencilan pada Model ARIMA dengan Bayesian Information Criterion (BIC) Termodifikasi“. STATISTIKA: Journal of Theoretical Statistics and Its Applications 19, Nr. 1 (20.06.2019): 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 are criteria that must be considered in order to produce the best model. The good criteria for models selection can use the Bayesian Information Criterion (BIC) derived by Schwarz (1978). Galeano and Pena (2011) proposed a modified Bayesian Information Criterion for model selection and detect potential outliers. The modified Bayesian Information Criterion for outlier detection will be applied to the data OutStanding Loan PT.Pegadaian Cimahi year 2013-2017. So that the best model is obtained that the model with adding 2 potential outliers with the ARIMA model (1.0,0), that outliers at observations 48, and 58 because it has a minimum BICUP value of 1064.95650.

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Hu, Wei, und 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 frequency information. The FKOID algorithm extracts the frequency information of each interval by means of Fast Fourier Transform, so as to calculate the distances between frequency features, and adopts the KNN method to measure the outlier score according to the sum of distances between the interval’s frequency vector and theKnearest frequency vectors. It detects the outlier intervals in a refined way at an appropriate expense of the time and is valid to detect the outlier intervals in both frequency and time domains.

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Yu, Yufeng, Yuelong Zhu, Shijin Li und 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 value and confidence coefficient. The use ofPCIas threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.

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Serras, Jorge L., Susana Vinga und Alexandra M. Carvalho. „Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks“. Applied Sciences 11, Nr. 4 (23.02.2021): 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 capable of capturing conditional dependencies in MTS datasets. A sliding window mechanism is employed to score each MTS transition gradually, given the DBN model. Two score-analysis strategies are studied to assure an automatic classification of anomalous data. The proposed approach is first validated in simulated data, demonstrating the performance of the system. Further experiments are made on real data, by uncovering anomalies in distinct scenarios such as electrocardiogram series, mortality rate data, and written pen digits. The developed system proved beneficial in capturing unusual data resulting from temporal contexts, being suitable for any MTS scenario. A widely accessible web application employing the complete system is publicly available jointly with a tutorial.

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Galeano, Pedro, Daniel Peña und Ruey S. Tsay. „Outlier Detection in Multivariate Time Series by Projection Pursuit“. Journal of the American Statistical Association 101, Nr. 474 (01.06.2006): 654–69. http://dx.doi.org/10.1198/016214505000001131.

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19

Abuzaid, A. H., I. B. Mohamed und A. G. Hussin. „Procedures for outlier detection in circular time series models“. Environmental and Ecological Statistics 21, Nr. 4 (24.04.2014): 793–809. http://dx.doi.org/10.1007/s10651-014-0281-8.

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20

Silva, Maria Eduarda, Isabel Pereira und Brendan McCabe. „Bayesian Outlier Detection in Non‐Gaussian Autoregressive Time Series“. Journal of Time Series Analysis 40, Nr. 5 (02.12.2018): 631–48. http://dx.doi.org/10.1111/jtsa.12439.

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21

Li, Gen, und Jason J. Jung. „Dynamic graph embedding for outlier detection on multiple meteorological time series“. PLOS ONE 16, Nr. 2 (18.02.2021): 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 distance between two graphs. DynGPE can cluster similar climatic events in the embedding space. Abnormal climatic events are distant from most of the other events and can be detected using outlier detection methods. We conducted experiments by applying three outlier detection methods (i.e., isolation forest, local outlier factor, and box plot) to real meteorological data. The results showed that DynGPE achieves better results than the baseline by 44.3% on average in terms of the F-measure. Isolation forest provides the best performance and stability. It achieved higher results than the local outlier factor and box plot methods, namely, by 15.4% and 78.9% on average, respectively.

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Anto Praveena, M. D., und 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, Nr. 8 (01.08.2020): 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 may simply experimental errors or novelty. Outlier identification is the method of identifying data objects with somewhat different behaviors than expectations. Detecting outliers in time series data is different from normal data. Time series data are the data that are in a series of certain time periods. This kind of data are identified and cleared to bring the quality dataset. In this proposed work a hybrid outlier detection algorithm extended LSTM-GAN is helped to recognize the outliers in time series data. The outcome of the proposed extended algorithm attained better enactment in the time series analysis on ECG dataset processing compared with traditional methodologies.

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Bui, Anh Tuan, und Chi-Hyuck Jun. „An Improved Iterative Procedure for Outlier Detection in Time Series“. Journal of Korean Institute of Industrial Engineers 38, Nr. 1 (01.03.2012): 17–24. http://dx.doi.org/10.7232/jkiie.2012.38.1.017.

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Kaliyaperumal, Senthamarai Kannan, Manoj Kuppusamy und Arumugam Subbanna Gounder. „Outlier Detection and Missing Value in Time Series Ozone Data“. International Journal of Scientific Research in Knowledge 3, Nr. 9 (01.09.2015): 220–26. http://dx.doi.org/10.12983/ijsrk-2015-p0220-0226.

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Lu, Jun, Lei Shi und Fei Chen. „Outlier Detection in Time Series Models Using Local Influence Method“. Communications in Statistics - Theory and Methods 41, Nr. 12 (15.06.2012): 2202–20. http://dx.doi.org/10.1080/03610926.2011.558664.

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Su, Wei-xing, Yun-long Zhu, Fang Liu und Kun-yuan Hu. „On-line outlier and change point detection for time series“. Journal of Central South University 20, Nr. 1 (Januar 2013): 114–22. http://dx.doi.org/10.1007/s11771-013-1466-2.

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Benkabou, Seif-Eddine, Khalid Benabdeslem und Bruno Canitia. „Unsupervised outlier detection for time series by entropy and dynamic time warping“. Knowledge and Information Systems 54, Nr. 2 (08.06.2017): 463–86. http://dx.doi.org/10.1007/s10115-017-1067-8.

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Baragona, Roberto, und Francesco Battaglia. „Outliers Detection in Multivariate Time Series by Independent Component Analysis“. Neural Computation 19, Nr. 7 (Juli 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 outliers consists of superimposing a deterministic disturbance to a gaussian multivariate time series. Then outliers may be modeled as nongaussian time series components. Independent component analysis is a recently developed tool that is likely to be able to extract possible outlier patterns. In practice, independent component analysis may be used to analyze multivariate observable time series and separate regular and outlying unobservable components. In the factor models framework too, it is shown that independent component analysis is a useful tool for detection of outliers in multivariate time series. Some algorithms that perform independent component analysis are compared. It has been found that all algorithms are effective in detecting various types of outliers, such as patches, level shifts, and isolated outliers, even at the beginning or the end of the stretch of observations. Also, there is no appreciable difference in the ability of different algorithms to display the outlying observations pattern.

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Ye, Feng, Zihao Liu, Qinghua Liu und Zhijian Wang. „Hydrologic Time Series Anomaly Detection Based on Flink“. Mathematical Problems in Engineering 2020 (28.05.2020): 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 data. Finally, based on the historical batch data, the K-Means++ algorithm is used to cluster the batch data. The state transition probability is calculated, and the anomaly data are evaluated in quality. Taking the hydrological sensor data obtained from the Chu River as experimental data, experiments on the detection time and outlier detection performance are carried out, respectively. The results show that when calculating the tens of millions of data, the time costed by two slaves is less than that by one slave, and the maximum reduction is 17.43%. The sensitivity of the evaluation is increased from 72.91% to 92.98%. In terms of delay, the average delay of different slaves is roughly the same, which is maintained within 20 ms. It shows that, under big data platform, the proposed algorithm can effectively improve the computational efficiency of hydrologic time series detection for tens of millions of data and has a significant improvement in sensitivity.

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Feng Lin, Wang Le und Jin Bo. „Research on Maximal Frequent Pattern Outlier Factor for Online High-Dimensional Time-Series Outlier Detection“. Journal of Convergence Information Technology 5, Nr. 10 (31.12.2010): 66–71. http://dx.doi.org/10.4156/jcit.vol5.issue10.9.

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Weekley, R. Andrew, Robert K. Goodrich und Larry B. Cornman. „An Algorithm for Classification and Outlier Detection of Time-Series Data“. Journal of Atmospheric and Oceanic Technology 27, Nr. 1 (01.01.2010): 94–107. http://dx.doi.org/10.1175/2009jtecha1299.1.

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Abstract An algorithm to perform outlier detection on time-series data is developed, the intelligent outlier detection algorithm (IODA). This algorithm treats a time series as an image and segments the image into clusters of interest, such as “nominal data” and “failure mode” clusters. The algorithm uses density clustering techniques to identify sequences of coincident clusters in both the time domain and delay space, where the delay-space representation of the time series consists of ordered pairs of consecutive data points taken from the time series. “Optimal” clusters that contain either mostly nominal or mostly failure-mode data are identified in both the time domain and delay space. A best cluster is selected in delay space and used to construct a “feature” in the time domain from a subset of the optimal time-domain clusters. Segments of the time series and each datum in the time series are classified using decision trees. Depending on the classification of the time series, a final quality score (or quality index) for each data point is calculated by combining a number of individual indicators. The performance of the algorithm is demonstrated via analyses of real and simulated time-series data.

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Plazas-Nossa, Leonardo, Miguel Antonio Ávila Angulo und Andres Torres. „Detection of Outliers and Imputing of Missing Values for Water Quality UV-VIS Absorbance Time Series“. Ingeniería 22, Nr. 1 (30.01.2017): 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 study comprising three sites in Colombia ((i) Bogotá D.C. Salitre-WWTP (Waste Water Treatment Plant), influent; (ii) Bogotá D.C. Gibraltar Pumping Station (GPS); and, (iii) Itagüí, San Fernando-WWTP, influent (Medellín metropolitan area)) analysed via UV-Vis (Ultraviolet and Visible) spectra.Results: Outlier detection with the proposed method obtained promising results when window parameter values are small and self-similar, despite that the three time series exhibited different sizes and behaviours. The DFT allowed to process different length gaps having missing values. To assess the validity of the proposed method, continuous subsets (a section) of the absorbance time series without outlier or missing values were removed from the original time series obtaining an average 12% error rate in the three testing time series.Conclusions: The application of the DFT and the IFFT, using the 10% most important harmonics of useful values, can be useful for its later use in different applications, specifically for time series of water quality and quantity in urban sewer systems. One potential application would be the analysis of dry weather interesting to rain events, a feat achieved by detecting values that correspond to unusual behaviour in a time series. Additionally, the result hints at the potential of the method in correcting other hydrologic time series.

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Tian, Bo, Dian Hong Wang, Fen Xiong Chen und Zheng Pu Zhang. „Based on ETEO Pattern Abnormal Event Detection in Wireless Sensor Networks“. Advanced Materials Research 926-930 (Mai 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 finally uses local outlier factor to detect abnormal pattern in this feature space. Experiments on synthetic and real data show that the definition of pattern outlier is reasonable and this algorithm is efficient to detect outliers in WSN.

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Zhao, Jianjun, Junwu Zhoub, Weixing Su und Fang Liu. „Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process“. Earth Sciences Research Journal 21, Nr. 3 (01.07.2017): 135–39. http://dx.doi.org/10.15446/esrj.v21n3.65215.

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Given the difficulty of accurate online detection for massive data collecting real-timely in a strong noise environment during the complex geological mineral grade analysis process, an order self-learning ARHMM (Autoregressive Hidden Markov Model) algorithm is proposed to carry out online outlier detection in the geological mineral grade analysis process. The algorithm utilizes AR model to fit the time series obtained from “Online x - ray Fluorescent Mineral Analyzer” and makes use of HMM as a basic detection tool, which can avoid the deficiency of presetting the threshold in traditional detection methods. The structure of traditional BDT (Brockwell-Dahlhaus-Trindade) algorithm is improved to be a double iterative structure in which iterative calculation from both time and order is applied respectively to update parameters of ARHMM online. With the purpose of reducing the influence of outlier on parameter update of ARHMM, the strategies of detection-before-update and detection-based-update are adopted, which also improve the robustness of the algorithm. Subsequent simulation by model data and practical application verify the accuracy, robustness, and property of online detection of the algorithm. According to the result, it is obvious that new algorithm proposed in this paper is more suitable for outlier detection of mineral grade analysis data in geology and mineral processing.

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Motta, Anderson C. O., und Luiz K. Hotta. „Detection of Patches of Outliers in Stochastic Volatility Processes“. São Paulo Journal of Mathematical Sciences 8, Nr. 2 (12.12.2014): 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 according to the stochastic volatility, a sample of the New York Stock Exchange daily returns, and a sample of the Brazilian S~ao Paulo Stock Exchange daily returns.

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Jiadong REN, Hongna LI, Changzhen HU und Haitao HE. „ODMC: Outlier Detection on Multivariate Time Series Data based on Clustering“. Journal of Convergence Information Technology 6, Nr. 2 (28.02.2011): 70–77. http://dx.doi.org/10.4156/jcit.vol6.issue2.8.

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Loperfido, Nicola. „Kurtosis-based projection pursuit for outlier detection in financial time series“. European Journal of Finance 26, Nr. 2-3 (02.08.2019): 142–64. http://dx.doi.org/10.1080/1351847x.2019.1647864.

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Rasheed, Faraz, und Reda Alhajj. „A Framework for Periodic Outlier Pattern Detection in Time-Series Sequences“. IEEE Transactions on Cybernetics 44, Nr. 5 (Mai 2014): 569–82. http://dx.doi.org/10.1109/tsmcc.2013.2261984.

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Ha, M. H., und S. Kim. „A Study on Outlier Detection Method for Financial Time Series Data“. Korean Journal of Applied Statistics 23, Nr. 1 (28.02.2010): 41–47. http://dx.doi.org/10.5351/kjas.2010.23.1.041.

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Thomas, Guy, Genevieve Plu und Jean-Christophe Thalabard. „Identification of pulses in hormone time series using outlier detection methods“. Statistics in Medicine 11, Nr. 16 (1992): 2133–45. http://dx.doi.org/10.1002/sim.4780111610.

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Baragona, Roberto, Francesco Battaglia und Domenico Cucina. „Empirical Likelihood for Outlier Detection and Estimation in Autoregressive Time Series“. Journal of Time Series Analysis 37, Nr. 3 (22.07.2015): 315–36. http://dx.doi.org/10.1111/jtsa.12145.

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Basu, Sabyasachi, und Martin Meckesheimer. „Automatic outlier detection for time series: an application to sensor data“. Knowledge and Information Systems 11, Nr. 2 (22.08.2006): 137–54. http://dx.doi.org/10.1007/s10115-006-0026-6.

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Hau, M. C., und H. Tong. „A practical method for outlier detection in autoregressive time series modelling“. Stochastic Hydrology and Hydraulics 3, Nr. 4 (Dezember 1989): 241–60. http://dx.doi.org/10.1007/bf01543459.

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Marczak, Martyna, und Tommaso Proietti. „Outlier detection in structural time series models: The indicator saturation approach“. International Journal of Forecasting 32, Nr. 1 (Januar 2016): 180–202. http://dx.doi.org/10.1016/j.ijforecast.2015.04.005.

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Gómez, Andrés. „Outlier Detection in Time Series via Mixed-Integer Conic Quadratic Optimization“. SIAM Journal on Optimization 31, Nr. 3 (Januar 2021): 1897–925. http://dx.doi.org/10.1137/19m1306233.

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Che Rose, Farid Zamani, Mohd Tahir Ismail und Mohd Hanafi Tumin. „Outliers detection in state-space model using indicator saturation approach“. Indonesian Journal of Electrical Engineering and Computer Science 22, Nr. 3 (01.06.2021): 1688. http://dx.doi.org/10.11591/ijeecs.v22.i3.pp1688-1696.

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Structural changes that occur due to outliers may reduce the accuracy of an estimated time series model, shifting the mean distribution and causing forecast failure. This study used general-to-specific approach to detect outliers via indicator saturation approach in the local level model framework. Focusing on impulse indicator saturation, performance recorded by the suggested approach was evaluated using Monte Carlo simulations. To tackle the issue of higher number of regressors compared to the number of observations, this research utilized the split-half approach algorithm. We found that the impulse indicator saturation performance relies heavily on the size of outlier, location of outlier and number of splits in the series examined. Detection of outliers using sequential and non-sequential algorithms is the most crucial issue in this study. The sequential searching algorithm was able to outperform the non-sequential searching algorithm in eliminating the non-significant indicators based on potency and gauge. The outliers captured using impulse indicator saturation in financial times stock exchange (FTSE) United States of America (USA) shariah index correspond to the financial crisis in 2008-2009.

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Lee, H. Andy, und Yer Van Hui. „Outliers detection in time series“. Journal of Statistical Computation and Simulation 45, Nr. 1-2 (Februar 1993): 77–95. http://dx.doi.org/10.1080/00949659308811473.

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Weng, Xiaoqing, und Junyi Shen. „Detecting outlier samples in multivariate time series dataset“. Knowledge-Based Systems 21, Nr. 8 (Dezember 2008): 807–12. http://dx.doi.org/10.1016/j.knosys.2008.03.048.

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Wang, Hai Lei, Wen Bo Li und Bing Yu Sun. „Support Vector Clustering for Outlier Detection“. Advanced Materials Research 756-759 (September 2013): 493–96. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.493.

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In this paper a novel Support vector clustering (SVC) method for outlier detection is proposed. Outlier detection algorithms have application in several tasks such as data mining, data preprocessing, data filter-cleaner, time series analysis and so on. Traditionally outlier detection methods are mostly based on modeling data based on its statistical properties and these approaches are only preferred when large scale set is available. To solve this problem, in this paper we focus on establishing the context of support vector clustering approach for outlier detection. Compared to traditional outlier detection methods , the performance of the SVC is not sensitive to the selection of needed parameters. The experiment results proved the efficiency of our method.

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S., Kannan, und Somasundaram K. „Autoregressive-based outlier algorithm to detect money laundering activities“. Journal of Money Laundering Control 20, Nr. 2 (02.05.2017): 190–202. http://dx.doi.org/10.1108/jmlc-07-2016-0031.

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Purpose Due to the large-size, non-uniform transactions per day, the money laundering detection (MLD) is a time-consuming and difficult process. The major purpose of the proposed auto-regressive (AR) outlier-based MLD (AROMLD) is to reduce the time consumption for handling large-sized non-uniform transactions. Design/methodology/approach The AR-based outlier design produces consistent asymptotic distributed results that enhance the demand-forecasting abilities. Besides, the inter-quartile range (IQR) formulations proposed in this paper support the detailed analysis of time-series data pairs. Findings The prediction of high-dimensionality and the difficulties in the relationship/difference between the data pairs makes the time-series mining as a complex task. The presence of domain invariance in time-series mining initiates the regressive formulation for outlier detection. The deep analysis of time-varying process and the demand of forecasting combine the AR and the IQR formulations for an effective outlier detection. Research limitations/implications The present research focuses on the detection of an outlier in the previous financial transaction, by using the AR model. Prediction of the possibility of an outlier in future transactions remains a major issue. Originality/value The lack of prior segmentation of ML detection suffers from dimensionality. Besides, the absence of boundary to isolate the normal and suspicious transactions induces the limitations. The lack of deep analysis and the time consumption are overwhelmed by using the regression formulation.

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