Relevant bibliographies by topics / Time series outlier detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Time series outlier detection'

Author: Grafiati
Published: 28 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Time series outlier detection"

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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 (March 5, 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, 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 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, and Hyung Jun Choa. "Outlier detection in time series data." Korean Journal of Applied Statistics 29, no. 5 (August 31, 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 (December 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 (May 1989): 241–48. http://dx.doi.org/10.1080/00401706.1989.10488517.

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

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Tran, Trong Dinh, Toan Duy Dao, Tung So Vu, Dung Ngoc Luong, Chieu Dinh Vu, Son Ngoc Bui, and Hang Thi Ha. "Outlier detection in GNSS position time series." Science and Technology Development Journal 19, no. 2 (June 30, 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, and A. O. Aduobi. "OUTLIER DETECTION IN UNIVARIATE TIME SERIES DATA." Far East Journal of Theoretical Statistics 50, no. 2 (June 9, 2015): 143–51. http://dx.doi.org/10.17654/fjtsmar2015_143_151.

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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 (March 1, 2021): 042070. http://dx.doi.org/10.1088/1755-1315/723/4/042070.

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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 (June 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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Dissertations / Theses on the topic "Time series outlier detection"

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Sedman, Robin. "Online Outlier Detection in Financial Time Series." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228069.

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In this Master’s thesis, different models for outlier detection in financial time series are examined. The financial time series are price series such as index prices or asset prices. Outliers are, in this thesis, defined as extreme and false points, but this definition is also investigated and revised. Two different time series models are examined: an autoregressive (AR) and a generalized autoregressive conditional heteroskedastic (GARCH) time series model, as well as one test statistic method based on the GARCH model. Additionally, a nonparametric model is examined, which utilizes kernel density estimation in order to detect outliers. The models are evaluated by how well they detect outliers and how often they misclassify inliers as well as the run time of the models. It is found that all the models performs approximately equally good, on the data sets used in thesis and the simulations done, in terms of how well the methods find outliers, apart from the test static method which performs worse than the others. Furthermore it is found that definition of an outlier is very crucial to how well a model detects the outliers. For the application of this thesis, the run time is an important aspect, and with this in mind an autoregressive model with a Student’s t-noise distribution is found to be the best one, both with respect to how well it detects outliers, misclassify inliers and run time of the model.
I detta examensarbete undersöks olika modeller för outlierdetektering i finansiella tidsserier. De finansiella tidsserierna är prisserier som indexpriser eller tillgångspriser. Outliers är i detta examensarbete definierade som extrema och falska punkter, men denna definition undersöks och revideras också. Två olika tidsseriemodeller undersöks: en autoregressiv (AR) och en generel au-toregressiv betingad heteroskedasticitet1 (GARCH) tidsseriemodell, samt en hypotesprövning2 baserad på GARCH-modellen. Dessutom undersöks en icke-parametrisk modell, vilken använder sig utav uppskattning av täthetsfunktionen med hjälp av kärnfunktioner3 för att detektera out-liers. Modellerna utvärderas utifrån hur väl de upptäcker outliers, hur ofta de kategoriserar icke-outliers som outliers samt modellens körtid. Det är konstaterat att alla modeller ungefär presterar lika bra, baserat på den data som används och de simuleringar som gjorts, i form av hur väl outliers är detekterade, förutom metoden baserad på hypotesprövning som fungerar sämre än de andra. Vidare är det uppenbart att definitionen av en outlier är väldigt avgörande för hur bra en modell detekterar outliers. För tillämpningen av detta examensarbete, så är körtid en viktig faktor, och med detta i åtanke är en autoregressiv modell med Students t-brusfördelning funnen att vara den bästa modellen, både med avseende på hur väl den detekterar outliers, felaktigt detekterar inliers som outliers och modellens körtid.
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Wang, Dan Tong. "Outlier detection with data stream mining approach in high-dimenional time series data." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691091.

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Bergamelli, Michele. "Structural breaks and outliers detection in time-series econometrics : methods and applications." Thesis, City University London, 2015. http://openaccess.city.ac.uk/14868/.

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This thesis contributes to the econometric literature on structural breaks analysis and outliers detection in parametric linear models. The focus is on the development of new econometric tools as well as on the analysis of novel but largely unexplored approaches. The econometric methods under analysis are illustrated using macroeconomic and financial relationships. The thesis is organised in three main chapters. In Chapter 2, we consider two novel methods to detect multiple structural breaks affecting the deterministic component of a linear system. The first is an extension of the dummy saturation method whereas the second method deals with a sequential bootstrapping procedure based on the sup-F statistic. Through an extensive Monte Carlo exercise, we explore the ability of the two approaches to detect the correct number and the correct location of the breaks. Additionally, we illustrate how to apply empirically the two procedures by investigating the stability of the Fisher relationship in the United States. In Chapter 3, we consider testing for multiple structural breaks in the vector error correction framework. First, we study the role of weak exogeneity when testing for structural breaks in the cointegrating matrix. Second, we extend the existing likelihood ratio test of Hansen (2003) to the case of unknown break dates through the specification of a minimum p-value statistic with critical values approximated by bootstrapping. Monte Carlo simulations show that the proposed statistic has good finite sample properties whilst three small empirical applications illustrate how the minimum p-value statistic can be used in practice. In Chapter 4, we tackle the purchasing power parity puzzle developing a robust estimator for the half-life of the real exchange rate. Specifically, we propose to identify outlying observations by means of a dummy saturation type algorithm designed for ARMA processes which enables to detect additional and innovative outliers as well as level shifts. An empirical application involving US dollar real exchange rates shows that the estimated half-lives are considerably shorter when outlying observations are correctly modelled, therefore shedding some light on the purchasing power parity puzzle.
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Eghbalian, Amirmohammad. "Data mining techniques for modeling the operating behaviors of smart building control valve systems." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20102.

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Background. One of the challenges about smart control valves system is processing and analyzing sensors data to extract useful information. These types of information can be used to detect the deviating behaviors which can be an indication of faults and issues in the system. Outlier detection is a process in which we try to find these deviating behaviors that occur in the system.Objectives. First, perform a literature review to get an insight about the machine learning (ML) and data mining (DM) techniques that can be applied to extract patternfrom time-series data. Next, model the operating behaviors of the control valve system using appropriate machine learning and data mining techniques. Finally,evaluate the proposed behavioral models on real world data.Methods. To have a better understanding of the different ML and DM techniques for extracting patterns from time-series data and fault detection and diagnosis of building systems, literature review is conducted. Later on, an unsupervised learning approach is proposed for modeling the typical operating behaviors and detecting the deviating operating behaviors of the control valve system. Additionally, the proposed method provides supplementary information for domain experts to help them in their analysis.Results. The outcome from modeling and monitoring the operating behaviors ofthe control valve system are analyzed. The evaluation of the results by the domain experts indicates that the method is capable of detecting deviating or unseen operating behaviors of the system. Moreover, the proposed method provides additional useful information to have a better understanding of the obtained results.Conclusions. The main goal in this study was achieved by proposing a method that can model the typical operating behaviors of the control valve system. The generated model can be used to monitor the newly arrived daily measurements and detect the deviating or unseen operating behaviors of the control valve system. Also, it provides supplementary information that can help domain experts to facilitate and reduce the time of analysis.
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Slávik, Ľuboš. "Dynamická faktorová analýza časových řad." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-445469.

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Táto diplomová práca sa zaoberá novým prístupom k zhlukovaniu časových rád na základe dynamického faktorového modelu. Dynamický faktorový model je technika redukujúca dimenziu a rozširuje klasickú faktorovú analýzu o požiadavku autokorelačnej štruktúry latentných faktorov. Parametre modelu sa odhadujú pomocou EM algoritmu za použitia Kalmanovho filtra a vyhladzovača a taktiež sú aplikované nevyhnutné podmienky na model, aby sa stal identifikovateľným. Po tom, ako je v práci predstavený teoretický koncept prístupu, dynamický faktorový model je aplikovaný na skutočné pozorované časové rady a práca skúma jeho správanie a vlastnosti na jednomesačných meteorologických dátach požiarneho indexu (Fire Weather Index) na 108 požiarnych staniciach umiestnených v Britskej Kolumbii. Postup výpočtu modelu odhadne záťažovú maticu (loadings matrix) spolu so zodpovedajúcim malým počtom latentných faktorov a kovariačnou maticou modelovaných časových rád. Diplomová práca aplikuje k-means zhlukovanie na výslednú záťažovú maticu a ponúka rozdelenie meteorologických staníc do zhlukov založené na redukovanej dimenzionalite pôvodných dát. Vďaka odhadnutým priemerom zhlukov a odhadnutým latentným faktorom je možné získať aj priemerné trendy každého zhluku. Následne sú dosiahnuté výsledky porovnané s výsledkami získanými na dátach z rovnakých staníc avšak iného mesiaca, aby sa stanovila stabilita zhlukovania. Práca sa taktiež zaoberá efektom varimax rotácie záťažovej matice. Diplomová práca naviac navrhuje metódu detekovania odľahlých časových rád založenú na odhadnutej kovariačnej matici modelu a rozoberá dôsledky odľahlých hodnôt na odhanutý model.
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Allab, Nedjmeddine. "Détection d'anomalies et de ruptures dans les séries temporelles. Applications à la gestion de production de l'électricité." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066658.

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Continental est l'outil de référence utilisé par EDF pour la gestion d'électricité à long terme. il permet d'élaborer la stratégie d'exploitation du parc constitué de centrales réparties sur toute l'europe. l'outil simule sur chaque zone et chaque scénario plusieurs variables telles que la demande d'électricité, la quantité générée ainsi que les coûts associés. nos travaux de thèse ont pour objectif de fournir des méthodes d'analyse de ces données de production afin de faciliter leur étude et leur synthèse. nous récoltons un ensemble de problématiques auprès des utilisateurs de continental que nous tentons de résoudre à l'aide des technique de détection d'anomalies et de ruptures dans les séries temporelles
Continental is the main tool that edf uses for the long-term management of electricity. It elaborates the strategy exploitation of the electrical parc made up by power plants distributed all over europe. the tool simulates for each zone and each scenario several variables, such as the electricity demand, the generated quantity as well as the related costs. our works aim to provide methods to analyse the data of electricity production in order to ease their discovery and synthesis. we get a set of problmatics from the users of continental that we tent to solve through techniques of outliers and changepoints detection in time series
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Ribeiro, Joana Patrícia Bordonhos. "Outlier identification in multivariate time series." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/22200.

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Mestrado em Matemática e Aplicações
Com o desenvolvimento tecnológico, existe uma cada vez maior disponibilidade de dados. Geralmente representativos de situações do dia-a-dia, a existência de grandes quantidades de informação tem o seu interesse quando permite a extração de valor para o mercado. Além disso, surge importância em analisar não só os valores disponíveis mas também a sua associação com o tempo. A existência de valores anormais é inevitável. Geralmente denotados como outliers, a procura por estes valores é realizada comummente com o intuito de fazer a sua exclusão do estudo. No entanto, os outliers representam muitas vezes um objetivo de estudo. Por exemplo, no caso de deteção de fraudes bancárias ou no diagnóstico de doenças, o objetivo central é identificar situações anormais. Ao longo desta dissertação é apresentada uma metodologia que permite detetar outliers em séries temporais multivariadas, após aplicação de métodos de classificação. A abordagem escolhida é depois aplicada a um conjunto de dados real, representativo do funcionamento de caldeiras. O principal objetivo é identificar as suas falhas. Consequentemente, pretende-se melhorar os componentes do equipamento e portanto diminuir as suas falhas. Os algoritmos implementados permitem identificar não só as falhas do aparelho mas também o seu funcionamento normal. Pretende-se que as metodologias escolhidas sejam também aplicadas nos aparelhos futuros, permitindo melhorar a identificação em tempo real das falhas.
With the technological development, there is an increasing availability of data. Usually representative of day-to-day actions, the existence of large amounts of information has its own interest when it allows to extract value to the market. In addition, it is important to analyze not only the available values but also their association with time. The existence of abnormal values is inevitable. Usually denoted as outliers, the search for these values is commonly made in order to exclude them from the study. However, outliers often represent a goal of study. For example, in the case of bank fraud detection or disease diagnosis, the central objective is to identify the abnormal situations. Throughout this dissertation we present a methodology that allows the detection of outliers in multivariate time series, after application of classification methods. The chosen approach is then applied to a real data set, representative of boiler operation. The main goal is to identify faults. It is intended to improve boiler components and, hence, reduce the faults. The implemented algorithms allow to identify not only the boiler faults but also their normal operation cycles. We aim that the chosen methodologies will also be applied in future devices, allowing to improve real-time fault identification.
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Åkerström, Emelie. "Real-time Outlier Detection using Unbounded Data Streaming and Machine Learning." Thesis, Luleå tekniska universitet, Datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80044.

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Accelerated advancements in technology, the Internet of Things, and cloud computing have spurred an emergence of unstructured data that is contributing to rapid growth in data volumes. No human can manage to keep up with monitoring and analyzing these unbounded data streams and thus predictive and analytic tools are needed. By leveraging machine learning this data can be converted into insights which are enabling datadriven decisions that can drastically accelerate innovation, improve user experience, and drive operational efficiency. The purpose of this thesis is to design and implement a system for real-time outlier detection using unbounded data streams and machine learning. Traditionally, this is accomplished by using alarm-thresholds on important system metrics. Yet, a static threshold cannot account for changes in trends and seasonality, changes in the system, or an increased system load. Thus, the intention is to leverage machine learning to instead look for deviations in the behavior of the data not caused by natural changes but by malfunctions. The use-case driving the thesis forward is real-time outlier detection in a Content Delivery Network (CDN). The input data includes Http-error messages received by clients, and contextual information like region, cache domains, and error codes, to provide tailormade predictions accounting for the trends in the data. The outlier detection system consists of a data collection pipeline leveraging the technique of stream processing, a MiniBatchKMeans clustering model that provides online clustering of incoming data according to their similar characteristics, and an LSTM AutoEncoder that accounts for temporal nature of the data and detects outlier data points in the clusters. An important finding is that an outlier is defined as an abnormal amount of outlier data points all originating from the same cluster, not a single outlier data point. Thus, the alerting system will be implementing an outlier percentage threshold. The experimental results show that an outlier is detected within one minute from a cache break-down. This triggers an alert to the system owners, containing graphs of the clustered data to narrow down the search area of the cause to enable preventive action towards the prominent incident. Further results show that within 2 minutes from fixing the cause the system will provide feedback that the actions taken were successful. Considering the real-time requirements of the CDN environment, it is concluded that the short delay for detection is indeed real-time. Proving that machine learning is indeed able to detect outliers in unbounded data streams in a real-time manner. Further analysis shows that the system is more accurate during peakhours when more data is in circulation than during none peak-hours, despite the temporal LSTM layers. Presumably, an effect from the model needing to train on more data to better account for seasonality and trends. Future work necessary to put the outlier detection system in production thus includes more training to improve accuracy and correctness. Furthermore, one could consider implementing necessary functionality for a production environment and possibly adding enhancing features that can automatically avert incidents detected and handle the causes of them.
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Sánchez, Enríquez Heider Ysaías. "Anomaly detection in streaming multivariate time series." Tesis, Universidad de Chile, 2017. http://repositorio.uchile.cl/handle/2250/149078.

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Doctor en Ciencias, Mención Computación
Este trabajo de tesis presenta soluciones para al problema de detección de anomalı́as en flujo de datos multivariantes. Dado una subsequencia de serie temporal (una pequeña parte de la serie original) como entrada, uno quiere conocer si este corresponde a una observación normal o es una anomalı́a, con respecto a la información histórica. Pueden surgir dificultades debido principalmente a que los tipos de anomalı́a son desconocidos. Además, la detección se convierte en una tarea costosa debido a la gran cantidad de datos y a la existencia de variables de dominios heterogéneos. En este contexto, se propone un enfoque de detección de anomalı́as basado en Discord Discovery, que asocia la anomalı́a con la subsecuencia más inusual utilizando medidas de similitud. Tı́picamente, los métodos de reducción de la dimensionalidad y de indexación son elaborados para restringir el problema resolviéndolo eficientemente. Adicionalmente, se propone técnicas para generar modelos representativos y consisos a partir de los datos crudos con el fin de encontrar los patrones inusuales. Estas técnicas también mejoran la eficiencia en la búsqueda mediante la reducción de la dimensionalidad. Se aborda las series multivariantes usando técnicas de representación sobre subsequencias no- normalizadas, y se propone nuevas técnicas de discord discovery basados en ı́ndices métricos. El enfoque propuesto es comparado con técnicas del estado del arte. Los resultados ex- perimentales demuestran que aplicando la transformación de translación y representación de series temporales pueden contribuir a mejorar la eficacia en la detección. Además, los métodos de indexación métrica y las heurı́sticas de discord discovery pueden resolver eficien- temente la detección de anomalı́as en modo offline y online en flujos de series temporales multivariantes.
Este trabajo ha sido financiado por beca CONICYT - CHILE / Doctorado para Extranjeros, y apoyada parcialmente por el Proyecto FONDEF D09I1185 y el Programa de Becas de NIC Chile
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Tinawi, Ihssan. "Machine learning for time series anomaly detection." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123129.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 55).
In this thesis, I explored machine learning and other statistical techniques for anomaly detection on time series data obtained from Internet-of-Things sensors. The data, obtained from satellite telemetry signals, were used to train models to forecast a signal based on its historic patterns. When the prediction passed a dynamic error threshold, then that point was flagged as anomalous. I used multiple models such as Long Short-Term Memory (LSTM), autoregression, Multi-Layer Perceptron, and Encoder-Decoder LSTM. I used the "Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding" paper as a basis for my analysis, and was able to beat their performance on anomaly detection by obtaining an F0.5 score of 76%, an improvement over their 69% score.
by Ihssan Tinawi.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Books on the topic "Time series outlier detection"

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Olympia, Hadjiliadis, ed. Quickest detection. Cambridge: Cambridge University Press, 2009.

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Cédric, Demeure, ed. Statistical signal processing: Detection, estimation, and time series analysis. Reading, Mass: Addison-Wesley Pub. Co., 1991.

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Riazuddin, Riaz. Detection and forecasting of Islamic calendar effects in Time Series Data. Karachi: State Bank of Pakistan, 2002.

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Time-frequency analysis and synthesis of linear signal spaces: Time-frequency filters, signal detection and estimation, and range-Doppler estimation. Boston: Kluwer Academic Publishers, 1998.

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Hlawatsch, F. Time-Frequency Analysis and Synthesis of Linear Signal Spaces: Time-Frequency Filters, Signal Detection and Estimation, and Range-Doppler Estimation. Boston, MA: Springer US, 1998.

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Radzeijewski, Maciej. Development, use and application of the Hydrospect data analysis system for the detection of changes in hydrological time series for use in WCP-water and national hydrological services. Poznań, Poland: World Meteorological Organization, 2004.

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Panova, Anna. Tourism statistics. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1046178.

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Contains a detailed overview of the basic concepts of the General theory of statistics, groups of statistics, absolute, relative and average values, statistical study of the relationship of socio-economic phenomena, time series and methods for the detection of trend in time series, indices and their use in tourism. The theoretical material is illustrated with examples from tourism and hospitality. Detail the history of the development, the subject and objectives, the indicator system of tourism statistics. Meets the requirements of Federal state educational standards of higher education of the last generation. For undergraduate students, graduate destinations 43.03.02, 43.04.02 "Tourism" and 43.03.03, 43.04.03 "Hospitality". It will be useful to employees of organizations of tourism, as well as receiving the second higher economic education.
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An Influence method for outlier detection applied to time series traffic data. Institute for Transport Studies, University of Leeds, 1992.

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Robust Regression and Outlier Detection (Wiley Series in Probability and Statistics). Wiley-Interscience, 2003.

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Dijk, Dick van, Andri Lucas, and Philip H. Franses. Outlier Robust Analysis of Economic Time Series (Advanced Texts in Econometrics). Oxford University Press, USA, 2005.

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Book chapters on the topic "Time series outlier detection"

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Aggarwal, Charu C. "Time Series and Multidimensional Streaming Outlier Detection." In Outlier Analysis, 273–310. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47578-3_9.

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Aggarwal, Charu C. "Time Series and Multidimensional Streaming Outlier Detection." In Outlier Analysis, 225–65. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-6396-2_8.

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Wang, Xiaochun, Xiali Wang, and Mitch Wilkes. "Unsupervised Fraud Detection in Environmental Time Series Data." In New Developments in Unsupervised Outlier Detection, 257–77. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9519-6_10.

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Landauer, Max, Markus Wurzenberger, Florian Skopik, Giuseppe Settanni, and Peter Filzmoser. "Time Series Analysis: Unsupervised Anomaly Detection Beyond Outlier Detection." In Information Security Practice and Experience, 19–36. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99807-7_2.

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Karioti, Vassiliki, and Polychronis Economou. "Detection of Outlier in Time Series Count Data." In Contributions to Statistics, 209–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55789-2_15.

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Gołaszewski, Grzegorz. "Similarity-Based Outlier Detection in Multiple Time Series." In Advances in Intelligent Systems and Computing, 116–31. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18058-4_10.

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van de Wiel, L., D. M. van Es, and A. J. Feelders. "Real-Time Outlier Detection in Time Series Data of Water Sensors." In Advanced Analytics and Learning on Temporal Data, 155–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65742-0_11.

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Khoshrou, Abdolrahman, and Eric J. Pauwels. "Data-Driven Pattern Identification and Outlier Detection in Time Series." In Advances in Intelligent Systems and Computing, 471–84. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01174-1_35.

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Amarbayasgalan, Tsatsral, Heon Gyu Lee, Pham Van Huy, and Keun Ho Ryu. "Deep Reconstruction Error Based Unsupervised Outlier Detection in Time-Series." In Intelligent Information and Database Systems, 312–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42058-1_26.

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Kha, Nguyen Huy, and Duong Tuan Anh. "From Cluster-Based Outlier Detection to Time Series Discord Discovery." In Lecture Notes in Computer Science, 16–28. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25660-3_2.

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Conference papers on the topic "Time series outlier detection"

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Akouemo, Hermine N., and Richard J. Povinelli. "Time series outlier detection and imputation." In 2014 IEEE Power & Energy Society General Meeting. IEEE, 2014. http://dx.doi.org/10.1109/pesgm.2014.6939802.

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Zwilling, Chris E., and Michelle Yongmei Wang. "Multivariate voronoi outlier detection for time series." In 2014 Health Innovations and POCT. IEEE, 2014. http://dx.doi.org/10.1109/hic.2014.7038934.

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Ferdousi, Z., and A. Maeda. "Unsupervised Outlier Detection in Time Series Data." In 22nd International Conference on Data Engineering Workshops (ICDEW'06). IEEE, 2006. http://dx.doi.org/10.1109/icdew.2006.157.

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Kieu, Tung, Bin Yang, Chenjuan Guo, and Christian S. Jensen. "Outlier Detection for Time Series with Recurrent Autoencoder Ensembles." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/378.

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We propose two solutions to outlier detection in time series based on recurrent autoencoder ensembles. The solutions exploit autoencoders built using sparsely-connected recurrent neural networks (S-RNNs). Such networks make it possible to generate multiple autoencoders with different neural network connection structures. The two solutions are ensemble frameworks, specifically an independent framework and a shared framework, both of which combine multiple S-RNN based autoencoders to enable outlier detection. This ensemble-based approach aims to reduce the effects of some autoencoders being overfitted to outliers, this way improving overall detection quality. Experiments with two large real-world time series data sets, including univariate and multivariate time series, offer insight into the design properties of the proposed frameworks and demonstrate that the resulting solutions are capable of outperforming both baselines and the state-of-the-art methods.
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Wang, Xin. "Two-phase outlier detection in multivariate time series." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019794.

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"A Deep Autoencoder based Outlier Detection for Time Series." In 2018 3rd International Conference on Computer Science and Information Engineering. Clausius Scientific Press, 2018. http://dx.doi.org/10.23977/iccsie.2018.1038.

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Smith, Gavin, and James Goulding. "A novel symbolization technique for time-series outlier detection." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7364037.

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Mehrang, Saeed, Elina Helander, Misha Pavel, Angela Chieh, and Ilkka Korhonen. "Outlier detection in weight time series of connected scales." In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359896.

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MacEachern, Leonard, and Ghazaleh Vazhbakht. "Configurable FPGA-Based Outlier Detection for Time Series Data." In 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2020. http://dx.doi.org/10.1109/mwscas48704.2020.9184548.

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Wang, Jin, Fang Miao, Lei You, and Wenjie Fan. "A Deep Autoencoder Based Outlier Detection for Time Series." In 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2020. http://dx.doi.org/10.1109/icpics50287.2020.9202041.

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Reports on the topic "Time series outlier detection"

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Grosskopf, Michael John. Aligning Time Series for Cyber-Physical Network Intrusion Detection. Office of Scientific and Technical Information (OSTI), August 2015. http://dx.doi.org/10.2172/1212612.

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Chen, Z., and S. E. Grasby. Detection of decadal and interdecadal oscillations and temporal trend analysis of climate and hydrological time series, Canadian Prairies. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2009. http://dx.doi.org/10.4095/248138.

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Rose-Pehrsson, Susan, Sean J. Hart, Mark H. Hammond, Daniel T. Gottuk, and Mark T. Wright. Real-Time Probabilistic Neural Network Performance and Optimization for Fire Detection and Nuisance Alarm Rejection: Test Series 2 Results. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada383627.

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Berney, Ernest, Naveen Ganesh, Andrew Ward, J. Newman, and John Rushing. Methodology for remote assessment of pavement distresses from point cloud analysis. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40401.

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The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-based sensors. ERDC-developed algorithms automatically discretize the pavement surface into cross- and grid-based sections to identify physical surface distresses such as depressions, ruts, and cracks. Depressions can be sized from the point-to-point distances bounding each depression, and surface roughness is determined based on the point heights along a given cross section. Noted distresses are exported to a distress map file containing only the distress points and their locations for later visualization and quality control along with classification and quantification. Further research and automation into point cloud analysis is ongoing with the goal of enabling Soldiers with limited training the capability to rapidly assess pavement surface condition from a remote platform.
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