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1. Evaluation of the application of sequence data to the identification of outbreaks of disease using anomaly detection methods

   https://doi.org/10.1186/s13567-023-01197-3  

   Díaz-Cao, José Manuel; Liu, Xin; Kim, Jeonghoon; Clavijo, Maria Jose; Martínez-López, Beatriz (December 2023, Veterinary Research)

   Abstract Anomaly detection methods have a great potential to assist the detection of diseases in animal production systems. We used sequence data of Porcine Reproductive and Respiratory Syndrome (PRRS) to define the emergence of new strains at the farm level. We evaluated the performance of 24 anomaly detection methods based on machine learning, regression, time series techniques and control charts to identify outbreaks in time series of new strains and compared the best methods using different time series: PCR positives, PCR requests and laboratory requests. We introduced synthetic outbreaks of different size and calculated the probability of detection of outbreaks (POD), sensitivity (Se), probability of detection of outbreaks in the first week of appearance (POD1w) and background alarm rate (BAR). The use of time series of new strains from sequence data outperformed the other types of data but POD, Se, POD1w were only high when outbreaks were large. The methods based on Long Short-Term Memory (LSTM) and Bayesian approaches presented the best performance. Using anomaly detection methods with sequence data may help to identify the emergency of cases in multiple farms, but more work is required to improve the detection with time series of high variability. Our results suggest a promising application of sequence data for early detection of diseases at a production system level. This may provide a simple way to extract additional value from routine laboratory analysis. Next steps should include validation of this approach in different settings and with different diseases. 

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2. Detecting confounders in multivariate time series using strength of causation

   Y. Liu, C (January 2023, EUSIPCO)

   One of the most important problems in science is understanding causation. This problem is particularly challenging when causation has to be inferred from observational data only. A further challenge of this problem is if the observed data were generated in the presence of latent confounders. In this paper, we propose a method for detecting confounders in multivariate time series using a recently introduced concept referred to as differential causal effect (DCE). The solution is based on feature-based Gaussian processes that are not only used for estimating the DCE of the observed time series but also for estimating the latent confounders. We demonstrate the performance of the proposed method with several examples. They show that the proposed approach can detect confounders and can accurately estimate causal strengths. 

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3. Estimation of prediction error in time series

   https://doi.org/10.1093/biomet/asad053  

   Aue, Alexander; Burman, Prabir (September 2023, Biometrika)

   Abstract Summary The accurate estimation of prediction errors in time series is an important problem. It immediately affects the accuracy of prediction intervals but also the quality of a number of widely used time series model selection criteria such as AIC and others. Except for simple cases, however, it is difficult or even infeasible to obtain exact analytical expressions for one-step and multi-step predictions. This may be one of the reasons that, unlike in the independent case (see Efron, 2004), until today there has been no fully established methodology for time series prediction error estimation. Starting from an approximation to the bias-variance decomposition of the squared prediction error, this work is therefore concerned with the estimation of prediction errors in both univariate and multivariate stationary time series. In particular, several estimates are developed for a general class of predictors that includes most of the popular linear, nonlinear, parametric and nonparametric time series models used in practice, where causal invertible ARMA and nonparametric AR processes are discussed as lead examples. Simulation results indicate that the proposed estimators perform quite well in finite samples. The estimates may also be used for model selection when the purpose of modeling is prediction. 

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4. Experimental Comparison of Online Anomaly Detection Algorithms

   Freeman, C; Merriman, J; Beavers, I; Mueen, A (May 2019, The Thirty-Second International Flairs Conference)

   Anomaly detection methods abound and are used extensively in streaming settings in a wide variety of domains. But a strength can also be a weakness; given the vast number of methods, how can one select the best method for their application? Unfortunately, there is no one best way for all domains. Existing literature is focused on creating new anomaly detection methods or creating large frameworks for experimenting with multiple methods at the same time. As the literature continues to grow, extensive evaluation of every available anomaly detection method is not feasible. To reduce this evaluation burden, in this paper we present a framework to intelligently choose the optimal anomaly detection methods based on the characteristics the time series displays. We provide a comprehensive experimental validation of multiple anomaly detection methods over different time series characteristics to form guidelines. Applying our framework can save time and effort by surfacing the most promising anomaly detection methods instead of experimenting extensively with a rapidly expanding library of anomaly detection methods. 

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5. Mining Multivariate Discrete Event Sequences for Knowledge Discovery and Anomaly Detection

   https://doi.org/10.1109/DSN48063.2020.00067  

   Nie, Bin; Xu, Jianwu; Alter, Jacob; Chen, Haifeng; Smirni, Evgenia (June 2020, Proceedings of the 50th IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2020)

   null (Ed.)

   Modern physical systems deploy large numbers of sensors to record at different time-stamps the status of different systems components via measurements such as temperature, pressure, speed, but also the component's categorical state. Depending on the measurement values, there are two kinds of sequences: continuous and discrete. For continuous sequences, there is a host of state-of-the-art algorithms for anomaly detection based on time-series analysis, but there is a lack of effective methodologies that are tailored specifically to discrete event sequences. This paper proposes an analytics framework for discrete event sequences for knowledge discovery and anomaly detection. During the training phase, the framework extracts pairwise relationships among discrete event sequences using a neural machine translation model by viewing each discrete event sequence as a "natural language". The relationship between sequences is quantified by how well one discrete event sequence is "translated" into another sequence. These pairwise relationships among sequences are aggregated into a multivariate relationship graph that clusters the structural knowledge of the underlying system and essentially discovers the hidden relationships among discrete sequences. This graph quantifies system behavior during normal operation. During testing, if one or more pairwise relationships are violated, an anomaly is detected. The proposed framework is evaluated on two real-world datasets: a proprietary dataset collected from a physical plant where it is shown to be effective in extracting sensor pairwise relationships for knowledge discovery and anomaly detection, and a public hard disk drive dataset where its ability to effectively predict upcoming disk failures is illustrated. 

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