Correlated Time Series Research Articles

Comparative analyses of SARS-CoV-2 RNA concentrations in Detroit wastewater quantified with CDC N1, N2, and SC2 assays reveal optimal target for predicting COVID-19 cases

To monitor COVID-19 through wastewater surveillance, global researchers dedicated significant endeavors and resources to develop and implement diverse RT-qPCR or RT-ddPCR assays targeting different genes of SARS-CoV-2. Effective wastewater surveillance hinges on the appropriate selection of the most suitable assay, especially for resource-constrained regions where scant technical and socioeconomic resources restrict the options for testing with multiple assays. Further research is imperative to evaluate the existing assays through comprehensive comparative analyses. Such analyses are crucial for health agencies and wastewater surveillance practitioners in the selection of appropriate methods for monitoring COVID-19. In this study, untreated wastewater samples were collected weekly from the Detroit wastewater treatment plant, Michigan, USA, between January and December 2023. Polyethylene glycol precipitation (PEG) was applied to concentrate the samples followed by RNA extraction and RT-ddPCR. Three assays including N1, N2 (US CDC Real-Time Reverse Transcription PCR Panel for Detection of SARS-CoV-2), and SC2 assay (US CDC Influenza SARS-CoV-2 Multiplex Assay) were implemented to detect SARS-CoV-2 in wastewater. The limit of blank and limit of detection for the three assays were experimentally determined. SARS-CoV-2 RNA concentrations were evaluated and compared through three statistical approaches, including Pearson and Spearman's rank correlations, Dynamic Time Warping, and vector autoregressive models. N1 and N2 demonstrated the highest correlation and most similar time series patterns. Conversely, N2 and SC2 assay demonstrated the lowest correlation and least similar time series patterns. N2 was identified as the optimal target to predict COVID-19 cases. This study presents a rigorous effort in evaluating and comparing SARS-CoV-2 RNA concentrations quantified with N1, N2, and SC2 assays and their interrelations and correlations with clinical cases. This study provides valuable insights into identifying the optimal target for monitoring COVID-19 through wastewater surveillance.

Water Quality Prediction Model for the Pearl River Estuary Based on BiLSTM Improved with Attention Mechanism

To improve the accuracy and stability of water quality prediction in the Pearl River Estuary, a water quality prediction model was proposed based on BiLSTM improved with an attention mechanism. The feature attention mechanism was introduced to enhance the ability of the model to capture important features, and the temporal attention mechanism was added to improve the mining ability of time series correlation information and water quality fluctuation details. The new model was applied to the water quality prediction of eight estuaries of the Pearl River, and the prediction performance test, generalization ability test, and characteristic parameter expansion test were carried out. The results showed that:① The new model achieved high prediction accuracy in the water quality prediction of the Zhuhaidaqiao section. The root-mean-square error (RMSE) between the predicted value and the measured value was 0.004 1 mg·L-1, and the coefficient of determination (R2) was 98.3 %. Compared with that of Multi-BiLSTM, Multi-LSTM, BiLSTM, and LSTM, the results showed that the new model had the highest prediction accuracy, which verified the accuracy of the model. ② Both the number of training samples and the number of forecasting steps affected the prediction accuracy of the model, and the prediction accuracy of the model increased with the increase of the training samples. When predicting the total phosphorus of the Zhuhaidaqiao section, more than 240 training samples could obtain higher prediction accuracy. Increasing the number of prediction steps caused the prediction accuracy of the model to decline rapidly, and the reliability of the model prediction could not be guaranteed when the number of prediction steps was greater than 5. ③ When the new model was applied to the prediction of different water quality indexes in eight estuaries of the Pearl River, the prediction results had high precision and the model had strong generalization ability. The input data of upstream water quality, rainfall, and other characteristic parameters associated with the section prediction index of the object could improve the prediction accuracy of the model. Through many tests, the results showed that the new model could meet the requirements of precision, applicability, and expansibility of water quality prediction in the Pearl River Estuary and thus is a new exploration method for high-precision prediction of water quality in complex hydrodynamic environments.

The BT-SAM-Net: a new framework of end-to-end periodic time-series fault diagnosis for aero-pipelines systems

Accurate recognition of aero-engine pipeline faults is of great significance for engine maintenance costs and downtime. Pipeline signals have a strong periodic time series correlation under strong pump source pressure pulsation stimulation. However, very few studies have considered the correlation of features at pulsation period time points. Additionally, it is challenging to realize intelligent fault diagnosis of weak characteristics of pipeline faults due to the influence of vibration-noise coupling of aero-engines. The time information feature extraction model combined with self-attention mechanism (BT-SAM-Net), a newly created fault detection framework end-to-end time-series and anti-noise, is presented for the aero-pipeline in order to close the aforementioned research gaps. The primary goal of the proposed framework is to accomplish intelligent classification tasks by using the measured aero-pipeline raw data as the model input. Firstly, a two-way time series information fusion model is creatively designed, which is the first attempt to analyze the difference in time series correlation characteristics of faults for aero-pipelines. Secondly, The BT-SAM-Net framework incorporates the self-attention mechanism as an optimization tool to enhance the ultimate decision-making accuracy of the framework. Thirdly, the BT-SAM-Net framework was compared with 7 other methods. The results show the superiority and stability by demonstrating the BT-SAM-Net framework can identify various aero-pipeline fault states with greater accuracy.

Research on the Validity of Bootstrap LM-Error Test in Spatial Random Effect Models

Under the condition of non-classical distributed errors, the test for spatial dependence in spatial panel data models is still a problem waiting to be solved. In this paper, we apply the FDB (Fast Double Bootstrap) method to spatial panel data models to test spatial dependence. In order to research the validity of the Bootstrap LM-Error test in spatial random effect models under the condition that the error term obeys a normal distribution, heteroscedasticity, or time-series correlation, we construct Bootstrap LM-Error statistics and make use of Monte Carlo simulation from size distortion and power aspects to carry out our research. The Monte Carlo simulation results show that the asymptotic LM-Error test in the spatial random effects model has a large size of distortion when the error term disobeys classical distribution. However, the FDB LM-Error test can effectively correct the size distortion of the asymptotic test with the precondition that there is nearly no loss of power in the FDB test. Obviously, compared to the asymptotic LM-Error test, the FDB LM-Error test is a more valid method to test spatial dependence in a spatial random effects model.

Adversarial Attacks against Deep-Learning-Based Automatic Dependent Surveillance-Broadcast Unsupervised Anomaly Detection Models in the Context of Air Traffic Management.

Deep learning has shown significant advantages in Automatic Dependent Surveillance-Broadcast (ADS-B) anomaly detection, but it is known for its susceptibility to adversarial examples which make anomaly detection models non-robust. In this study, we propose Time Neighborhood Accumulation Iteration Fast Gradient Sign Method (TNAI-FGSM) adversarial attacks which fully take into account the temporal correlation of an ADS-B time series, stabilize the update directions of adversarial samples, and escape from poor local optimum during the process of iterating. The experimental results show that TNAI-FGSM adversarial attacks can successfully attack ADS-B anomaly detection models and improve the transferability of ADS-B adversarial examples. Moreover, the TNAI-FGSM is superior to two well-known adversarial attacks called the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM). To the best of our understanding, we demonstrate, for the first time, the vulnerability of deep-learning-based ADS-B time series unsupervised anomaly detection models to adversarial examples, which is a crucial step in safety-critical and cost-critical Air Traffic Management (ATM).

Wavelet-based correlations of the global magnetic field in connection to strongest earthquakes

We consider 3-component records of the magnetic field strength with a time step of 1 min at 153 stations of the INTERMAGNET network for 31 years, 1991–2021. Data analysis is based on the calculation of pairwise correlation coefficients between wavelet coefficients in successive time windows 1 day long (1440 min counts). To describe the state of the magnetic field, the maxima of the average values of all pairwise correlation coefficients between stations were chosen, calculated over all detail levels of the wavelet decomposition and over all components of the magnetic field strength vector. The daily time series of such maxima is called wavelet correlation. The division of the network stations into 7 clusters is considered, and a time series of wavelet correlations is calculated for each cluster. In a sliding time window with a length of 365 days, correlation measures of synchronization of wavelet correlations from different clusters are calculated, which are compared with the strongest earthquakes with a magnitude of at least 8.5. For the global time series of wavelet correlations, the method of influence matrices is used to study the relationship between the maximum correlation responses to a change in the length of the day and a sequence of earthquakes with a magnitude of at least 7. As a result of the analysis, precursor effects are identified, and the important role of the Maule earthquake in Chile on February 27, 2010 in the behavior of the response of magnetic field for the preparation of strong seismic events is shown.

Correlated Time Series Modeling of Carbon Emissions and Global Temperature Fluctuations

Correlated Time Series Modeling of Carbon Emissions and Global Temperature Fluctuations

Clustering of Wind Speed Time Series as a Tool for Wind Farm Diagnosis

In several industrial fields, environmental and operational data are acquired with numerous purposes, potentially generating a huge quantity of data containing valuable information for management actions. This work proposes a methodology for clustering time series based on the K-medoids algorithm using a convex combination of different time series correlation metrics, the COMB distance. The multidimensional scaling procedure is used to enhance the visualization of the clustering results, and a matrix plot display is proposed as an efficient visualization tool to interpret the COMB distance components. This is a general-purpose methodology that is intended to ease time series interpretation; however, due to the relevance of the field, this study explores the clustering of time series judiciously collected from data of a wind farm located on a complex terrain. Using the COMB distance for wind speed time bands, clustering exposes operational similarities and dissimilarities among neighboring turbines which are influenced by the turbines’ relative positions and terrain features and regarding the direction of oncoming wind. In a significant number of cases, clustering does not coincide with the natural geographic grouping of the turbines. A novel representation of the contributing distances—the COMB distance matrix plot—provides a quick way to compare pairs of time bands (turbines) regarding various features.

Distillation enhanced time series forecasting network with momentum contrastive learning

Contrastive representation learning is crucial in time series analysis as it alleviates the issue of data noise and incompleteness as well as sparsity of supervision signal. However, existing contrastive learning frameworks usually focus on intra-temporal features, which fail to fully exploit the intricate nature of time series data. To address this issue, we propose DE-TSMCL, an innovative distillation enhanced framework for long sequence time series forecasting. Specifically, we design a learnable data augmentation mechanism which adaptively learns whether to mask a timestamp to obtain optimized sub-sequences. Then, we propose a contrastive learning task with momentum update to explore inter-sample and intra-temporal correlations of time series to learn the underlying structure feature on the unlabeled time series. Meanwhile, we design a supervised task to learn more robust representations and facilitate the contrastive learning process. Finally, we jointly optimize the above two tasks. By developing model loss from multiple tasks, we can learn effective representations for downstream forecasting task. Extensive experiments, in comparison with state-of-the-arts, well demonstrate the effectiveness of DE-TSMCL, where the maximum improvement can reach to 27.3%. Source code for the algorithm is available at https://github.com/gaohaozhi/DE-TSMCL.

MMG-Based Knee Dynamic Extension Force Estimation Using Cross-Talk and IGWO-LSTM.

Mechanomyography (MMG) is an important muscle physiological activity signal that can reflect the amount of motor units recruited as well as the contraction frequency. As a result, MMG can be utilized to estimate the force produced by skeletal muscle. However, cross-talk and time-series correlation severely affect MMG signal recognition in the real world. These restrict the accuracy of dynamic muscle force estimation and their interaction ability in wearable devices. To address these issues, a hypothesis that the accuracy of knee dynamic extension force estimation can be improved by using MMG signals from a single muscle with less cross-talk is first proposed. The hypothesis is then confirmed using the estimation results from different muscle signal feature combinations. Finally, a novel model (improved grey wolf optimizer optimized long short-term memory networks, i.e., IGWO-LSTM) is proposed for further improving the performance of knee dynamic extension force estimation. The experimental results demonstrate that MMG signals from a single muscle with less cross-talk have a superior ability to estimate dynamic knee extension force. In addition, the proposed IGWO-LSTM provides the best performance metrics in comparison to other state-of-the-art models. Our research is expected to not only improve the understanding of the mechanisms of quadriceps contraction but also enhance the flexibility and interaction capabilities of future rehabilitation and assistive devices.

Development and Perspective of Gold and Silver Jewelry Imports to Slovakia Considering the Impact of the COVID-19 Pandemic

This study focuses on gold and silver jewelry import to Slovakia between the years 2010–2022 for hallmarking according to the country of origin. The aim of the research was to analyze and predict the import of gold and silver jewelry to Slovakia using statistical methods, namely the correlation coefficient and time series analysis. Using the correlation coefficient, we determined the relationship or degree of relation between selected countries of origin based on the amount of gold and silver jewelry (in kg) imported to Slovakia for hallmarking. We obtained an estimate of the future development trend of gold and silver imports into the domestic market from abroad using the time series method, particularly regression analysis and Holt-Winterson methods. It was found that the import of gold jewelry to Slovakia from the studied countries has an upward trend and, conversely, the import of silver jewelry has a downward trend.

Prediction of hourly wind speed time series at unsampled locations using machine learning

Various models for wind speed mapping have been developed, with increasing attention on models focusing on mapping wind speed distribution. This study extends these models to predict hourly wind speed time series at unsampled locations. A model based on the quantile mapping (QM) procedure was compared to a traditional and machine-learning model to interpolate wind speed spatially. These proposed models were also used with inputs from the ERA5 reanalysis dataset, enabling them to consider local variation in orography and large-scale wind fields. A widely used procedure for mean bias correction of reanalysis based on the Global Wind Atlas (GWA) was implemented and compared to the proposed models. It was found that the QM and machine learning model, both using input from ERA5, significantly outperformed GWA bias correction in terms of time series correlation and probability distribution. Despite being more computationally intensive than GWA bias correction, both models are recommended due to their significantly (in a statistical sense) superior performance.

Multivariate time series anomaly detection via separation, decomposition, and dual transformer-based autoencoder

Multivariate time series usually have entangled temporal patterns and various anomaly types. Meanwhile, they often contain both continuous and discrete features. Many existing methods directly model correlations in complex multivariate time series to conduct anomaly detection. Decomposing time series into different components, such as the overall trend and fluctuations, can contribute to better extracting semantic information and detecting anomalies. Existing decomposition-based anomaly detection methods still have several limitations. First, they directly decompose all features without considering that discrete features are unsuitable for decomposition because they do not have trends or fluctuations. Second, they adopt the same networks for different components with different characteristics, limiting their ability to extract semantic information. Moreover, due to the nature of Transformers, existing reconstruction-based methods using Transformers rarely form information bottlenecks, reducing the differentiation between the reconstruction errors of normal data and anomalies. This paper proposes a multivariate time series anomaly detection method with separation, decomposition, and dual Transformer-based autoencoder (SDDformer). Different from existing methods, SDDformer separates continuous and discrete features and only decomposes continuous features into trend and residual components. Considering the different characteristics of different components, SDDformer adopts Crossformer and the vanilla Transformer as the backbone of two different autoencoders to reconstruct the trend and residual components. Information bottlenecks are better formed using an extra token as the latent variable between the encoder and the decoder. SDDformer regards reconstructing a discrete feature as a classification task and calculates Cross-Entropy as its reconstruction error. Three different metrics are adopted in this paper to compare SDDformer with a variety of typical anomaly detection methods on public data sets, and the experimental results prove that SDDformer can achieve state-of-the-art performance.

Improved time-variable transformer-based fault diagnosis method for satellite attitude control system

This study proposes a fault diagnosis method based on a time-variable transformer network for satellite attitude control system telemetry data, which has the properties of a time series and strong multivariate correlation. First, a time-attention module was utilised to determine the correlation between the data at a specific time and before that time to effectively identify the dynamic properties of the data. A variable attention module is introduced to capture the degree of correlation between various variables to achieve multivariate decoupling. Then, the interactive attention layer combines the dynamic properties of the data with the correlations between the variables to achieve dynamic data decorrelation. Finally, a linear layer is used to implement the fault diagnosis.

Doomed to fail? A call to reform global climate governance and greenhouse gas inventories

Commitments to emissions reductions following the landmark Paris Climate Change Agreement have proliferated. Though it is promising that 145 countries have declared a net-zero emissions target, with 33 enshrining this goal into law, comparison of country-level emissions inventories can only be effectively carried out with uniform and consistent data. The extent to which greenhouse gas (GHG) inventory comparison is possible, and the ancillary climate governance implications, are the motivation for this article. Based on time-series correlation analyses over 32 years and 43 Annex-I countries, we uncover issues that are likely to inveigh against country-country comparison of GHGs—with the potential to weaken climate governance systems that are based mainly on emissions inventory tracking. First, the Global Warming Potentials (GWPs)—which convert each respective GHG into carbon equivalents (CO2-e), and are revised with each IPCC report—are not immediately or consistently integrated into GHG inventories. Second, GHGs apart from carbon dioxide, based on the data analysis, do not appear to be tracked uniformly. Should comparison of emissions remain a cornerstone of global climate governance, an overhaul of country-level GHG inventories is called for, specifically to enable effective reporting and tracking of GHGs apart from only carbon dioxide.

Physical mechanism-corrected degradation trend prediction network under data missing

Accurate degradation trend prediction (DTP) is crucial for optimizing equipment operation and maintenance, thereby boosting production efficiency. This study introduces a novel Data Repair and Dual-data-stream LSTM (DR-DLSTM) network to tackle the challenge of missing data in equipment DTP. The proposed DR-DLSTM framework employs convex optimization to consider both the trend and periodic variations in the data, incorporating polynomial and trigonometric functions into the implicit feature matrix to construct latent vectors for missing data rectification. The network features a Dual-LSTM block with dual data streams to enhance feature extraction, with two gating update units correlating time series components and redistributing feature weights. The Dual-LSTM enables separate and accurate prediction of trend and periodic components, thereby enhancing the feature extraction capability of the prediction model. Additionally, the integration of physical rule information through Fourier and wavelet transform frequency correction modules allows for dynamic adjustments in prediction outcomes, from global trends to localized details. The DR-DLSTM's effectiveness is demonstrated through comprehensive comparisons with state-of-the-art models across multiple datasets, highlighting its superior performance. The results demonstrate the superiority of the proposed model. These algorithms were implemented in Python using Torch on a 2.9 GHz Intel I7 CPU and TITAN Xp GPU.

An analysis and investigation of the factors affecting the air quality index in various cities in China

With the rapid progress of society, air pollution has gradually become the focus of people's concern, the impact of air pollution is very many factors, including but not limited to natural factors, human factors, geographic factors, etc., this paper is mainly from the natural factors and human factors, to explore its impact on air quality in the past five years, this paper first from the data itself, summarizes the distribution characteristics of the air quality in different regions of China, and then from the temperature and humidity throughout the year, to quantify its impact, and then with the help of time-series methods, to explore the air pollution indicators in the case of de-seasonalization of the air pollution indicators. This paper firstly summarizes the distribution characteristics of air quality in different regions of China from the data itself, then quantifies the impact of temperature and humidity throughout the year, and then explores whether the air pollution indicators satisfy the smooth distribution under the situation of de-seasonalization with the help of time series correlation method. The paper then compares the effects of several anthropogenic factors on the air through the gray correlation method. Finally, the influence of each factor in different parts of the country is summarized to provide reference for future studies.

Current Fluctuations in Open Quantum Systems: Bridging the Gap Between Quantum Continuous Measurements and Full Counting Statistics

Continuously measured quantum systems are characterized by an output current, in the form of a stochastic and correlated time series, which conveys crucial information about the underlying quantum system. The many tools used to describe current fluctuations are scattered across different communities: quantum opticians often use stochastic master equations, while a prevalent approach in condensed-matter physics is provided by full counting statistics. These, however, are simply different sides of the same coin. Our goal with this tutorial is to provide a unified toolkit for describing current fluctuations. This not only provides novel insights, by bringing together different fields in physics, but also yields various analytical and numerical tools for computing quantities of interest. We illustrate our results with various pedagogical examples and connect them with topical fields of research, such as waiting-time statistics, quantum metrology, thermodynamic uncertainty relations, quantum point contacts, and Maxwell’s demons. Published by the American Physical Society 2024

348 Alexithymia impacts vulnerability for cognitive decline in healthy elders via frontal lobe connectivity during response inhibition, especially in women

OBJECTIVES/GOALS: This project aimed to examine the impacts of biological sex and alexithymia on frontal lobe connectivity in executive functioning (EF)-related neural networks during successful inhibition as a means to index vulnerability for future cognitive decline. METHODS/STUDY POPULATION: Healthy, cognitively intact older adults (n=43, 33 female, Mage=79) completed the 20-item Toronto Alexithymia Scale (TAS-20) and the stop-signal task in this study. We used electroencephalography (EEG) source estimation to investigate EF-related frontal connectivity during successful inhibition in stop-signal task trials. Connectivity was measured in bilateral frontal ROIs relevant to inhibition using time series correlations over the N200 (186-350ms) and P300 (340-616ms) time windows, associated with the inhibitory subprocesses of conflict processing and performance evaluation, respectively. RESULTS/ANTICIPATED RESULTS: Those with higher alexithymia, as measured by the difficulty identifying feelings (DIF) facet of the TAS-20, had lower right anterior cingulate cortex (ACC)-left superior frontal gyrus (SFG) connectivity in the P300 window, suggesting impaired performance evaluation. Further, in females specifically, those with higher DIF had greater right inferior frontal gyrus (rIFG)-bilateral ACC connectivity in the N200 window than those with lower DIF, suggesting greater resources were allocated for conflict processing and inhibition. Right ACC-rIFG connectivity also correlated with better stop accuracy and faster stop-signal reaction time, supporting this network’s role in successful inhibition. DISCUSSION/SIGNIFICANCE: Overall, during successful inhibition, higher DIF was associated with reduced performance monitoring efficiency as well as greater resource allocation for conflict processing during motor stopping in women only. Thus, alexithymia (via DIF) may exacerbate age-related EF dysfunction and risk for future cognitive decline, especially for females.

Messages are Never Propagated Alone: Collaborative Hypergraph Neural Network for Time-Series Forecasting.

This paper delves into the problem of correlated time-series forecasting in practical applications, an area of growing interest in a multitude of fields such as stock price prediction and traffic demand analysis. Current methodologies primarily represent data using conventional graph structures, yet these fail to capture intricate structures with non-pairwise relationships. To address this challenge, we adopt dynamic hypergraphs in this study to better illustrate complex interactions, and introduce a novel hypergraph neural network model named CHNN for correlated time series forecasting. In more detail, CHNN leverages both semantic and topological similarities via an interaction model and hypergraph diffusion process, thereby constructing comprehensive collaborative correlation scores that effectively guide spatial message propagation. In addition, it incorporates short-term temporal information to generate efficient spatio-temporal feature maps. Lastly, a long-term temporal module is proposed to generate future predictions utilizing both temporal attention and a gated recurrent network. Comprehensive experiments conducted on four real-world datasets, i.e., Tiingo, Stocktwits, NYC-Taxi, and Social Network demonstrate that the proposed CHNN markedly outperforms a range of benchmark methods.