A fuzzy/Bayesian approach for the time series change point detection problem

There has been much attention in recent years to the problem of detecting mean changes in a piecewise constant time series. Often, methods assume that the noise can be taken to be independent, identically distributed (IID), which in practice may not be a reasonable assumption. There is comparatively little work studying the problem of mean changepoint detection in time series with nontrivial autocovariance structure. In this article, we propose a likelihood‐based method using wavelets to detect changes in mean in time series that exhibit time‐varying autocovariance. Our proposed technique is shown to work well for time series with a variety of error structures via a simulation study, and we demonstrate its effectiveness on two data examples arising in economics.

Change points are abrupt variations in time series data. Such abrupt changes may represent transitions that occur between states. Detection of change points is useful in modelling and prediction of time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. This survey article enumerates, categorizes, and compares many of the methods that have been proposed to detect change points in time series. The methods examined include both supervised and unsupervised algorithms that have been introduced and evaluated. We introduce several criteria to compare the algorithms. Finally, we present some grand challenges for the community to consider.

In the past paper, we have formulated the structural Change Point Detection (CPD) problem in time series data as an optimal stopping one using the concept of DP (Dynamic Programming). And, we have shown the optimal solution theorem and its proof using reduction to absurdity. In this paper, we present the direct and constructive proof of the optimal solution theorem for the problem.

Early detection of fuel leakage at service stations with underground petroleum storage systems is a crucial task to prevent catastrophic hazards. Current data-driven fuel leakage detection methods employ offline statistical inventory reconciliation, leading to significant detection delays. Consequently, this can result in substantial financial loss and environmental impact on the surrounding community. In this paper, we propose a novel framework called memory-based online change point detection (MOCPD) which operates in near real-time, enabling early detection of fuel leakage. MOCPD maintains a collection of representative historical data within a size-constrained memory, along with an adaptively computed threshold. Leaks are detected when the dissimilarity between the latest data and historical memory exceeds the current threshold. An update phase is incorporated in MOCPD to ensure diversity among historical samples in the memory. With this design, MOCPD is more robust and achieves a better recall rate while maintaining a reasonable precision score. We have conducted a variety of experiments comparing MOCPD to commonly used online change point detection (CPD) baselines on real-world fuel variance data with induced leakages, actual fuel leakage data and benchmark CPD datasets. Overall, MOCPD consistently outperforms the baseline methods in terms of detection accuracy, demonstrating its applicability to fuel leakage detection and CPD problems.

Wikipedia has been turned into an immensely popular crowd-sourced encyclopedia for information dissemination on numerous versatile topics in the form of subscription free content. It allows anyone to contribute so that the articles remain comprehensive and updated. For enrichment of content without compromising standards, the Wikipedia community enumerates a detailed set of guidelines, which should be followed. Based on these, articles are categorized into several quality classes by the Wikipedia editors with increasing adherence to guidelines. This quality assessment task by editors is laborious as well as demands platform expertise. As a first objective, in this paper, we study evolution of a Wikipedia article with respect to such quality scales. Our results show novel non-intuitive patterns emerging from this exploration. As a second objective we attempt to develop an automated data driven approach for the detection of the early signals influencing the quality change of articles. We posit this as a change point detection problem whereby we represent an article as a time series of consecutive revisions and encode every revision by a set of intuitive features. Finally, various change point detection algorithms are used to efficiently and accurately detect the future change points. We also perform various ablation studies to understand which group of features are most effective in identifying the change points. To the best of our knowledge, this is the first work that rigorously explores English Wikipedia article quality life cycle from the perspective of quality indicators and provides a novel unsupervised page level approach to detect quality switch, which can help in automatic content monitoring in Wikipedia thus contributing significantly to the CSCW community.

It is very often the case that at some moment a time series process abruptly changes its underlying structure and, therefore, it is very important to accurately detect such change-points. In this problem, which is called a change-point (or break-point) detection problem, we need to find a method that divides the original nonstationary time series into a piecewise stationary segments. In this paper, we develop a flexible method to estimate the unknown number and the locations of change-points in autoregressive time series. In order to find the optimal value of a performance function, which is based on the Minimum Description Length principle, we develop a Cross-Entropy algorithm for the combinatorial optimization problem. Our numerical experiments show that the proposed approach is very efficient in detecting multiple change-points when the underlying process has moderate to substantial variations in the mean and the autocorrelation coefficient. We also apply the proposed method to real data of daily AUD/CNY exchange rate series from 2 January 2018 to 24 March 2020.

Capturing correlation changes by applying kernel change point detection on the running correlations

This paper presents short-term forecasting methods applied to electricity consumption in Brazil. The focus is on comparing the results obtained after using two distinct approaches: dynamic non-linear models and econometric models. The first method, that we propose, is based on structural statistical models for multiple time series analysis and forecasting. It involves non-observable components of locally linear trends for each individual series and a shared multiplicative seasonal component described by dynamic harmonics. The second method, adopted by the electricity power utilities in Brazil, consists of extrapolation of the past data and is based on statistical relations of simple or multiple regression type. To illustrate the proposed methodology, a numerical application is considered with real data. The data represents the monthly industrial electricity consumption in Brazil from the three main power utilities: Eletropaulo, Cemig and Light, situated at the major energy-consuming states, Sao Paulo, Rio de Janeiro and Minas Gerais, respectively, in the Brazilian Southeast region. The chosen time period, January 1990 to September 1994, corresponds to an economically unstable period just before the beginning of the Brazilian Privatization Program. Implementation of the algorithms considered in this work was made via the statistical software S-PLUS. Copyright © 1999 John Wiley & Sons, Ltd.

Besides teaching in the education system, instructors do a bunch of background processes such as preparing study material, question paper setting, managing attendance, log book entry, student assessment, and the result analysis of the class. Moreover, Learning Management System(LMS) is mandatory if the course is online. The Massive Open Online Course (MOOC) is an example of the worldwide online education system. Nowadays, educators are using Google to efficiently formulate study material, question papers, and especially for self-preparation. Also, student assessment and result analysis tools are available to get instant results by feeding student data. Artificial Intelligence (AI) is driving behind these applications to deliver the most precise outcome. To accomplish that, AI requires historical data to train the model, and this sequential (year-wise, month-wise, etc) information is called time series data. This Systematic Literature Review (SLR) is conducted to find the contribution of time series algorithms in Education. There are enormous changes in algorithm architecture analogized to the traditional neural network to endure all kinds of data. Though it significantly raises the performance, it expands the complexity, resources, and execution time as well. Due to this, comprehending the algorithm architecture and the method of the execution process is a challenging phase before creating the model. But it is essential to have enough knowledge to select the suitable technique for the right solution. The first part reviews the time series problems in educational datasets using Deep Learning(DL). The second part describes the architecture of the time series model, such as the Recurrent Neural Network (RNN) and its variants called Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU), the differences between each other, and the classification of performance metrics. Finally, the factors affecting the time series model accuracy and the significance of this work are summarized to incite the people who desire to initiate the research in educational time series problems.

Context.Active regions on the photosphere of a star have been the major obstacle for detecting Earth-like exoplanets using the radial velocity (RV) method. A commonly employed solution for addressing stellar activity is to assume a linear relationship between the RV observations and the activity indicators along the entire time series, and then remove the estimated contribution of activity from the variation in RV data (overall correction method). However, since active regions evolve on the photosphere over time, correlations between the RV observations and the activity indicators will correspondingly be anisotropic.Aims.We present an approach that recognizes the RV locations where the correlations between the RV and the activity indicators significantly change in order to better account for variations in RV caused by stellar activity.Methods.The proposed approach uses a general family of statistical breakpoint methods, often referred to as change point detection (CPD) algorithms; several implementations of which are available in R and python. A thorough comparison is made between the breakpoint-based approach and the overall correction method. To ensure wide representativity, we use measurements from real stars that have different levels of stellar activity and whose spectra have different signal-to-noise ratios.Results.When the corrections for stellar activity are applied separately to each temporal segment identified by the breakpoint method, the corresponding residuals in the RV time series are typically much smaller than those obtained by the overall correction method. Consequently, the generalized Lomb–Scargle periodogram contains a smaller number of peaks caused by active regions. The CPD algorithm is particularly effective when focusing on active stars with long time series, such asαCen B. In that case, we demonstrate that the breakpoint method improves the detection limit of exoplanets by 74% on average with respect to the overall correction method.Conclusions.CPD algorithms provide a useful statistical framework for estimating the presence of change points in a time series. Since the process underlying the RV measurements generates anisotropic data by its intrinsic properties, it is natural to use CPD to obtain cleaner signals from RV data. We anticipate that the improved exoplanet detection limit may lead to a widespread adoption of such an approach. Our test on the HD 192310 planetary system is encouraging, as we confirm the presence of the two hosted exoplanets and we determine orbital parameters consistent with the literature, also providing much more precise estimates for HD 192310 c.

In finance, cluster analysis is a tool particularly useful for classifying stock market multivariate time series data related to daily returns, volatility daily stocks returns, commodity prices, volume trading, index, enhanced index tracking portfolio, and so on. In the literature, following different methodological approaches, several clustering methods have been proposed for clustering multivariate time series. In this paper by adopting a fuzzy approach and using the Partitioning Around Medoids strategy, we suggest to cluster multivariate financial time series by considering the dynamic time warping distance. In particular, we proposed a robust clustering method capable to neutralize the negative effects of possible outliers in the clustering process. The clustering method achieves its robustness by adopting a suitable trimming procedure to identify multivariate financial time series more distant from the bulk of data. The proposed clustering method is applied to the stocks composing the FTSE MIB index to identify common time patterns and possible outliers.

Change points in time series appear due to variations in the data generation process.We consider the problem of modeling time series generated by dynamic processes, and we focus on finding the change points using a specially tailored genetic algorithm.The algorithm employs a new representation, described in detail in the paper. Suitable genetic operators are also defined and explained.The results obtained on computer generated time series provide evidence that the approach can be used for change point detection, and has good potential for time series modeling.

Time series clustering is to assign a set of time series into groups that share certain similarity. It has become an attractive analytic tool as many applications require such classifications. Clustering may also result in more accurate parameter estimates when a group of time series are assumed to share common models and parameters, especially for short panel time series. Many existing time series clustering methods are based on the assumption that the time series are linear. However, linearity assumptions often fail to hold. In this paper we consider the problem of clustering nonlinear time series. We propose the use of a two dimensional Kolmogorov-Smirnov statistic as a distance measure of two time series by measuring the affinity of nonlinear serial dependence structures. It is nonparametric in nature hence no model assumption are needed. The approach is illustrated with simulation studies as well as real data examples.

By detecting the changing trend, Change point detection (CPD) can describe the underlying behavior of the system with time series (e.g., equipment failure detection, auxiliary medical diagnosis, and climate change detection, etc.). However, in the current big data environment, how quickly obtaining time-series data dependencies and accurately detecting change points is still a challenge. We propose Sailfish, an unsupervised change point detection framework based on the Gaussian process for time series data. In comparison with existing CPD designs, Sailfish has two novel features: 1) using the deep Gaussian process as a hidden variable transformer and 2) integrating the Gaussian process into a bi-LSTM cell for capturing past and future embedded feature trends. Our extensive experiments show that the Sailfish significantly outperforms five state-of-the-art CPD methods with a faster speed and higher accuracy on three public real-time-series datasets. Especially on the large volume of datasets, Sailfish can achieve up to a 60% reduction in training time and 13% F1-score improvement with little overhead compared to the state-of-the-arts.KeywordsChange point detectionGaussian processBi-Long short term memoryBayesian neural network

Time series data with multiple channels, known as multivariate time series (MTS), are increasingly common in modern applications. A common task in this context is subsequence search, which involves finding subsequences within large MTS datasets that closely match a given query pattern. While researchers have extensively studied this problem for univariate time series, applying these methods to multiple channels presents key challenges, including higher dimensionality and the need for flexible query specifications. Existing MTS search approaches are limited - they typically only match complete time series rather than subsequences, or restrict searches to predetermined channel combinations. This paper introduces MULISSE, a novel algorithm for exact variable-length k-NN subsequence search on multivariate time series. Extending the state-of-the-art algorithm for univariate time series (ULISSE), our approach incorporates multivariate symbolic approximations and refined node splitting strategies to efficiently handle multiple channels. Our experimental evaluation demonstrates the complexity of the problem: while achieving up to two orders of magnitude speedup on synthetic datasets with many channels, the gains over baseline methods on real-world datasets are more modest, ranging from 10% to 2x. The method's effectiveness is heavily influenced by factors such as time series length, number of channels, and the index's hyperparameters, highlighting both the potential and current limitations of index-based approaches for MTS search. As a first step towards efficient variable-length MTS search, this work not only demonstrates the viability of index-based methods but also identifies several promising directions for future improvements in areas such as dynamic SAX representations and cost-effective exhaustive distance computation.