A survey of methods for time series change point detection

Aminikhanghahi, Samaneh; Cook, Diane J.

doi:10.1007/s10115-016-0987-z

Cited by 920 publications

(598 citation statements)

References 54 publications

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“…Other methods that can be found, especially in applications, are the piecewise regression, the extraction of the signal of a turning point from a leading indicator series (particularly popular in business cycle literature), and the probabilistic event-oriented procedures (for an overview see eg, refs. [1,4]).…”

Section: A Review Of Turning Point Detection Methodsmentioning

confidence: 99%

Online detection of financial time series peaks and troughs: A probability‐based approach*

Bramante

Facchinetti

Zappa

2019

Statistical Analysis

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The problem related to the identification of a change in time series trajectories plays a crucial role in many contexts. In this paper, we propose a flexible and computationally efficient procedure for turning point identification based on hypothesis testing applied to the difference between two consecutive slopes in a rolling regression framework. Along with the description of the methodology, to measure the performance of the method we have applied it to the S&P 500 Stock Index and its subsector indices. By using an in‐sample/out‐of‐sample approach we compare results with the profit/losses we could obtain by using the moving average crossover strategy. Results show that the operating signals obtained by our proposal may better enable financial analysts to make profitable decisions. Finally we present an extensive simulation study to show the weaknesses and strengths of the proposal under different expected returns and volatility scenarios.

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Section: A Review Of Turning Point Detection Methodsmentioning

confidence: 99%

Online detection of financial time series peaks and troughs: A probability‐based approach*

Bramante

Facchinetti

Zappa

2019

Statistical Analysis

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“…Kalman filters can extend these basic ideas to combine multiple sources of information when the information is possibly noisy. Change point detection methods [129] can also be applied to time series data such as smart home sensor firings. In this case, data from two consecutive time periods are examined to determine if they come from the same probability distribution.…”

Section: Detecting and Assessing Threatsmentioning

confidence: 99%

Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

Dahmen

Cook

Wang

et al. 2017

J Reliable Intell Environ

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Smart home design has undergone a metamorphosis in recent years. The field has evolved from designing theoretical smart home frameworks and performing scripted tasks in laboratories. Instead, we now find robust smart home technologies that are commonly used by large segments of the population in a variety of settings. Recent smart home applications are focused on activity recognition, health monitoring, and automation. In this paper, we take a look at another important role for smart homes: security. We first explore the numerous ways smart homes can and do provide protection for their residents. Next, we provide a comparative analysis of the alternative tools and research that has been developed for this purpose. We investigate not only existing commercial products that have been introduced but also discuss the numerous research that has been focused on detecting and identifying potential threats. Finally, we close with open challenges and ideas for future research that will keep individuals secure and healthy while in their own homes.

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“…In general, the problem concerns detecting whether or not a change has occurred, or whether several changes might have occurred, and identifying the times of any such changes . A lot of work has been done in this research field and it is impossible to give an exhaustive overview (see, eg, the work of Aminikhanghahi and Cook). The most widely used methods for online change detecting have been developed within the Statistical Process Control (SPC) framework .…”

Section: Introductionmentioning

confidence: 99%

Novelty detection based on learning entropy

Dohnal

Bukovský

2019

Appl Stoch Models Bus & Ind

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The Approximate Individual Sample Learning Entropy is based on incremental learning of a predictor truex˜false(k+hfalse)=ϕfalse(boldxfalse(kfalse),boldwfalse), where x(k) is an input vector of a given size at time k, w is a vector of weights (adaptive parameters), and h is a prediction horizon. The basic assumption is that, after the underlying process x changes its behavior, the incrementally learning system will adapt the weights w to improve the predictor truex˜. Our goal is to detect a change in the behavior of the weight increment process. The main idea of this paper is based on the fact that weight increments △w(k), where △w(k) = w(k + 1) − w(k), create a weakly stationary process until a change occurs. Once a novelty behavior of the underlying process x(k) occurs, the process △w(k) changes its characteristics (eg, the mean or variation). We suggest using convenient characteristics of △w(k) in a multivariate detection scheme (eg, the Hotelling's T2 control chart).

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A survey of methods for time series change point detection

Cited by 920 publications

References 54 publications

Online detection of financial time series peaks and troughs: A probability‐based approach*

Online detection of financial time series peaks and troughs: A probability‐based approach*

Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

Novelty detection based on learning entropy

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