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Books on the topic 'Time series outlier detection'

Author: Grafiati

Published: 28 June 2021

Last updated: 12 February 2022

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1

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

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2

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

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3

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

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4

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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5

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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6

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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7

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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8

An Influence method for outlier detection applied to time series traffic data. Institute for Transport Studies, University of Leeds, 1992.

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9

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

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10

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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11

Outlier Robust Analysis of Economic Time Series (Advanced Texts in Econometrics). Oxford University Press, 2008.

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12

Inclan, Carla H. Retrospective detection of sudden changes of variance in time series. 1991.

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13

Real-Time Collision Detection (The Morgan Kaufmann Series in Interactive 3-D Technology). Morgan Kaufmann, 2004.

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14

W, Kundzewicz Zbigniew, Unesco, World Meteorological Organization, and World Climate Programme, eds. Detection of change in world-wide hydrological time series of maximum annual flow. [S.l.]: World Meteorological Organization, 2004.

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15

Sabbe, Marc, K. Bronselaer, and O. Hoogmartens. The emergency medical system. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199687039.003.0007.

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The mission of the emergency medical services is to promote and support a system that provides timely, professional, and state-of-the art emergency medical care, including ambulance services, to anyone who is victim of a sudden injury or illness, at any time or location and at any phase of the emergency incident. These phases include lay people’s prevention and preparedness, occurrence of the problem, its detection, alarming of trained responders, help provided by bystanders and trained pre-hospital providers, transport to the appropriate hospital, and, if necessary, admission or transfer to a more appropriate hospital. In order to meet the goal outlined, emergency medical services must work closely with local and state officials—fire and rescue departments, other ambulance providers, hospitals, and other agencies—to foster a smooth functioning network. The term emergency medical services evolved to reflect a change from a simple system of ambulances, providing only transportation, to a system in which actual medical care is given at the scene and during transport. Medical supervision and/or participation of emergency medicine physicians in the emergency medical services systems contribute to the quality of medical care. This emergency medical services network must be capable of responding instantly and reliably around the clock, with well-trained, well-equipped personnel linked, as needed, through a strong communication system. Research plays an important role in conserving resources and improving the delivery of health care. This chapter gives an overview of the different aspects of emergency medical services and calls for high-quality research in pre-hospital emergency care in a true partnership between cardiologists and emergency physicians.

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16

Prado, Raquel. Multistate models for mental fatigue. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.29.

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This article discusses the use of structured, multivariate Bayesian dynamic models in the analysis of experimental data involving large-scale electroencephalography (EEG) signals or time series generated on individuals subject to tasks inducing mental fatigue. It first provides an overview of the goals and challenges in the analysis of brain signals, using the EEG case as example, before describing the development and application of novel time-varying autoregressive and regime switching models, which incorporate relevant prior information via structured priors and fitted using novel, customized Bayesian computational methods. In the experiment, a subject was asked to perform simple arithmetic operations for a period of three hours. Prior to the experiment, the subject was confirmed to be alert. After the experiment ended, the subject was fatigued. The study demonstrates that Bayesian analysis is useful for real time detection of cognitive fatigue.

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17

Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Entropy and Surrogate Testing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0005.

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Reconstructing real-world system dynamics from time series data on a single variable is challenging because real-world data often exhibit a highly volatile and irregular appearance potentially driven by several diverse factors. NLTS methods help eliminate less likely drivers of dynamic irregularity. We set a benchmark for regular behavior by investigating how linear systems of ODEs are restricted to exponential and periodic dynamics, and illustrating how irregular behavior can arise if regular linear dynamics are corrupted with noise or shift over time (i.e., nonstationarity). We investigate how data can be pre-processed to control for the noise and nonstationarity potentially camouflaging nonlinear deterministic drivers of observed complexity. We can apply signal-detection methods, such as Singular Spectrum Analysis (SSA), to separate signal from noise in the data, and test the signal for nonstationarity potentially corrected with SSA. SSA measures signal strength which provides a useful initial indicator of whether we should continue searching for endogenous nonlinear drivers of complexity. We begin diagnosing deterministic structure in an isolated signal by attempting to reconstructed a shadow attractor. Finally, we use the classic Lorenz equations to illustrate how a deterministic nonlinear system of ODEs with at least three equations can generate observed irregular dynamics endogenously without aid of exogenous shocks or nonstationary dynamics.

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