Changes In Time Series Research Articles

The impact of low-velocity shock waves on the dynamic behaviour characteristics of nanobubbles.

Low-velocity shock wave-induced contraction and expansion of nanobubbles can be applied to biocarriers and microfluidic systems. Although experiments have been conducted to study the application effects, the dynamic behavior characteristics of nanobubbles remain unexplored. In this work, we utilize molecular dynamics (MD) simulations to investigate the dynamic behavior characteristics of nanobubbles influenced by low-velocity shock waves in a liquid argon system. The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) machine learning method is used to calculate the equivalent radius of nanobubbles. Two statistical methods are then utilized to predict the time series changes in the equivalent radius of nanobubbles without rebound shock waves. The piston velocity is analyzed using the bisection method to obtain the critical impact states of the nanobubble. The results show that at the low velocity shock wave (piston velocity of 0.1 km s-1), the shock wave pressure is small, the non-vacuum nanobubbles contract and expand in a circular shape, and the gas particles inside the bubble are not dispersed. In contrast, the vacuum nanobubbles collapse directly. As the shock wave rebounds upon impact, it triggers periodic contraction and expansion of the nanobubbles. The predictions indicate that the equivalent radius will vary within a small range according to the pre-predicted values in the absence of the rebound shock wave. Nanobubbles are present in four critical impact states: dispersed gaps, multiple smaller bubbles, two split bubbles, and a concave bubble.

Towards Wearable and Portable Spine Motion Analysis Through Dynamic Optimization of Smartphone Videos and IMU Data.

Monitoring spine kinematics is crucial for applications like disease evaluation and ergonomics analysis. However, the small scale of vertebrae and the number of degrees of freedom present significant challenges for noninvasive and convenient spine kinematics estimation. This study developed a dynamic optimization framework for wearable spine motion tracking at the intervertebral joint level by integrating smartphone videos and Inertia Measurement Units (IMUs) with dynamic constraints from a thoracolumbar spine model. Validation involved motion data from 10 healthy males performing static standing, dynamic upright trunk rotations, and gait. This data included rotations of ten IMUs on vertebrae and virtual landmarks from three smartphone videos preprocessed by OpenCap, an application leveraging computer vision for pose estimation. The kinematic measures derived from the optimized solution were compared against simultaneously collected infrared optical marker-based measurements and in vivo literature data. Solutions only based on IMUs or videos were also compared for accuracy evaluation. The proposed optimization approach closely matched the reference data in the intervertebral or segmental rotation range, demonstrating minimal angular differences across all motions and the highest correlation in 3D rotations (maximal Pearson and intraclass correlation coefficients of 0.92 and 0.94, respectively). Time-series changes of joint angles also aligned well with the optical-marker reference. Dynamic optimization of the spine simulation that integrates IMUs and computer vision outperforms the single-modality method. This markerless 3D spine motion capture method holds potential for spinal health assessment in large cohorts in real-world settings without dedicated laboratories.

MEASURING STRUCTURAL CHANGES OF RECURRENCE PATTERNS IN MULTIFRACTAL AND MULTISCALE ASPECTS BY GENERALIZED RECURRENCE LACUNARITY

Recurrence lacunarity has been recently proposed to detect dynamical state transitions over various temporal scales. In this paper, we combine suggested distribution moments and introduce multifractal recurrence lacunarity to unearth rich information of trajectories in phase space. By considering generalized moments, it provides an enhanced measurement to account for differences of black pixels in the recurrence plot at various scales. Numerical simulations have proved that the proposed method is able to differentiate varying types of time series and provide further insights of inherent features including stochastic series, chaotic maps and series contaminated interference components. In real-world applications, it performs well on quantifying the subtle structural changes of financial time series. In addition, it is intriguing to confirm that corrugation signals possess much more vivid information of heterogeneity in terms of recurrence plots than normal ones.

Distributional Drift Adaptation With Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting.

Due to the nonstationary nature, the distribution of real-world multivariate time series (MTS) changes over time, which is known as distribution drift. Most existing MTS forecasting models greatly suffer from distribution drift and degrade the forecasting performance over time. Existing methods address distribution drift via adapting to the latest arrived data or self-correcting per the meta knowledge derived from future data. Despite their great success in MTS forecasting, these methods hardly capture the intrinsic distribution changes, especially from a distributional perspective. Accordingly, we propose a novel framework temporal conditional variational autoencoder (TCVAE) to model the dynamic distributional dependencies over time between historical observations and future data in MTSs and infer the dependencies as a temporal conditional distribution to leverage latent variables. Specifically, a novel temporal Hawkes attention (THA) mechanism represents temporal factors that subsequently fed into feedforward networks to estimate the prior Gaussian distribution of latent variables. The representation of temporal factors further dynamically adjusts the structures of Transformer-based encoder and decoder to distribution changes by leveraging a gated attention mechanism (GAM). Moreover, we introduce conditional continuous normalization flow (CCNF) to transform the prior Gaussian to a complex and form-free distribution to facilitate flexible inference of the temporal conditional distribution. Extensive experiments conducted on six real-world MTS datasets demonstrate the TCVAE's superior robustness and effectiveness over the state-of-the-art MTS forecasting baselines. We further illustrate the TCVAE applicability through multifaceted case studies and visualization in real-world scenarios.

Multi-Sensors Time Series Change Point Detection in Wireless Sensor Networks Based on Deep Evidential Fusion and Self Distillation Learning

Multi-Sensors Time Series Change Point Detection in Wireless Sensor Networks Based on Deep Evidential Fusion and Self Distillation Learning

Uneven time series forecasting using a modified exponential smoothing method

The article is devoted to the problem of forecasting time series with an uneven distribution of observations over time. The exponential smoothing model is used as the basic forecasting model, in which the variable weights of observations decrease exponentially. The exponential smoothing model allows us to take into account the attenuation of the correlation of cross sections of a random process of time series change over time. However, this does not take into account the factors of temporal unevenness of the results of observations and the finiteness of the sample of observations. The article describes a method for predicting an uneven time series based on a modified exponential smoothing model, in which the transition from exponential smoothing to decreasing non-exponential smoothing is carried out. The modified sequence of the weights of the observations is determined by adjusting the classically calculated exponential weights, taking into account the actual irregularity of the observations.

GeoAI Dataset for Rural Hazardous Facilities Segmentation from KOMPSAT Ortho Mosaic Imagery

In South Korea, rural areas have been recognized for their potential as sustainable spaces for the future, but they are currently facing major problems. Unplanned construction of facilities such as factories, livestock facilities, and solar panels near residential areas is destroying the rural environment and deteriorating the quality of life of residents. Detection and monitoring of rural facilities are necessary to prevent disorderly development in rural areas and to manage rural space in a planned manner. In this study, satellite imagery data was utilized to obtain information on rural areas, which is useful for observing large areas and monitoring time series changes compared to field surveys. In this study, KOMPSAT ortho-mosaic optical imagery from 2019 and 2020 were utilized to construct AI training datasets for rural hazardous facilities segmentation for Seosan, Anseong, Naju, and Geochang areas. The dataset can be used in image segmentation models to classify rural facilities and can be used to monitor potentially hazardous facilities in rural areas. It is expected to contribute to solving rural problems by serving as the basis for rural planning.

Improving user's sense of participation in robot-driven dialogue

ABSTRACT In this study, we aimed to enhance the sense of dialogue participation (SDP), which reflects an individual's conscious engagement in dialogues, and investigated the multimodal dialogue elements that contribute to its improvement. To achieve this, we developed a dialogue system utilizing an android robot specifically for tour spot recommendations at a travel agency. The system was designed with various components aimed at enhancing SDP and was evaluated by 29 participants during the Dialogue Robot Competition 2022. After conceptually defining SDP, we employed the author's subjective evaluation to visualize the time-series changes in SDP at approximately 20-second intervals, using video recordings of the dialogues. Initially, we expected an overall decrease in SDP, as the robot had a speaking duration of around two minutes during the middle of the dialogue session when presenting the tour plan. However, the analysis of five participants' data revealed an increase in SDP during segments that involved ‘Dealing with content that increases the user's interest’ and ‘Multimodal response requests.’ Consequently, we can conclude that these elements have a discernible impact on enhancing SDP.

A closer look at the time course of bodily responses to awe experiences

The structure and function of awe have been examined by focusing on the average level of outcomes during awe experiences. In the present study, we tested the psychophysiological process of experiencing awe, focusing on time-series changes in skin conductance responses (SCRs), a moment-by-moment measure of sympathetic nervous responses, and pupil diameter, which is dilated or constricted through the sympathetic and parasympathetic nervous system. The responses were measured through an experiment where 77 Japanese university students watched emotional (awe, amusement, or neutral) videos while moving a joystick when they felt supernatural agency or non-agency, examining the underlying psychological processes. We found that experiencing awe was associated with frequent and steep changes in SCRs and frequent changes in pupil diameter. The joystick inclination, the perceptions of the supernatural, was kept at a high level from the start to the end of awe experiences. These results may reflect the psychophysiological processes of awe: the “fluctuation” of the sympathetic nervous system might underlie awe-specific experiences. Our findings shed new light on the mechanisms of the body-mind interaction in awe experiences.

Detecting Structural Changes in Time Series by Using the BDS Test Recursively: An Application to COVID-19 Effects on International Stock Markets

Structural change tests aim to identify evidence of a structural break or change in the underlying generating process of a time series. The BDS test has its origins in chaos theory and seeks to test, using the correlation integral, the hypothesis that a time series is generated by an identically and independently distributed (IID) stochastic process over time. The BDS test is already widely used as a powerful tool for testing the hypothesis of white noise in the residuals of time series models. In this paper, we illustrate how the BDS test can be implemented also in a recursive manner to evaluate the hypothesis of structural change in a time series, taking advantage of its ability to test the IID hypothesis. We apply the BDS test repeatedly, starting with a sub-sample of the original time series and incrementally increasing the number of observations until it is applied to the full sample time series. A structural change in the unknown underlying generator model is detected when a change in the trend shown by this recursively computed BDS statistic is detected. The strength of this recursive BDS test lies in the fact that it does not require making any assumptions about the underlying time series generator model. We ilustrate the power and potential of this recursive BDS test through an application to real economic data. In this sense, we apply the test to assess the structural changes caused by the COVID-19 pandemic in international financial markets. Using daily data from the world’s top stock indices, we have detected strong and statistically significant evidence of two major structural changes during the period from June 2018 to June 2022. The first occurred in March 2020, coinciding with the onset of economic restrictions in the main Western countries as a result of the pandemic. The second occurred towards the end of August 2020, with the end of the main economic restrictions and the beginning of a new post-pandemic economic scenario. This methodology to test for structural changes in a time series is easy to implement and can detect changes in any system or process behind the time series even when this generating system is not known, and without the need to specify or estimate any a priori generating model. In this sense, the recursive BDS test could be incorporated as an initial preliminary step to any exercise of time series modeling. If a structural change is detected in a time series, rather than estimating a single predictive model for the full-sample time series, efforts should be made to estimate different predictive models, one for the time before and one for the time after the detected structural change.

Research on time series change point detection and influencing factors under machine learning: based on PM2.5 concentration data in Hefei city

Research on time series change point detection and influencing factors under machine learning: based on PM2.5 concentration data in Hefei city

Multivariate multiscale dispersion Lempel–Ziv complexity for fault diagnosis of machinery with multiple channels

Lempel–Ziv complexity (LZC), as a nonlinear feature in information science, has shown great promise in detecting correlations and capturing dynamic changes in single-channel time series. However, its application to multichannel data has been largely unexplored, while the complexity of real-world systems demands the utilization of data collected from multiple sensors or channels so as to extract distinguishable fault features for fault diagnosis. This paper proposes a novel method called multivariate multiscale dispersion Lempel–Ziv complexity (mvMDLZC) to extract the fault features hidden in multi-source information. First, multivariate embedding theory is applied to obtain multivariate embedded vectors and multivariate dispersion patterns, which can reflect the inherent relationships in the multichannel series. Second, by assigning labels to these patterns, the original multichannel time series can be transformed into a symbolic sequence with multiple symbols instead of the original binary conversion, enabling the accurate recovery of the system dynamics. Finally, the complexity counter value and normalized LZC are calculated for the complexity measure. Experimental results using synthetic and real-world datasets demonstrate that mvMDLZC outperforms existing LZC-based methods and multivariate dispersion entropy in recognizing different states of mechanical systems. Additionally, mvMDLZC exhibits robustness in handling challenges such as small sample datasets and noise interference, making it suitable for real industrial applications. These findings highlight the potential of mvMDLZC as a valuable approach for dissecting multichannel systems across various real-world scenarios.

Long-time monitoring of seagrass beds on the east coast of Hainan Island based on remote sensing images

Seagrass beds have extremely high ecological service value, so their protection and management are very important. The coastal area that stretches from Wenchang to Qionghai in the eastern part of Hainan Island is the largest seagrass bed distribution area in China’s South China Sea Bioregion. Understanding the local distribution and changes in seagrass beds is a prerequisite for implementing the protection and management of seagrass bed resources in this area. In this study, Landsat satellite remote sensing images of the study area from 1986 to 2021 were selected; seagrass bed survey data and field investigation data were combined; the Sum Green Index was used to extract and plot seagrass bed distribution information; the spatial distribution changes in seagrass beds in the region over the past thirty years were analyzed; and the driving factors of these changes are discussed. The following conclusions were drawn: (i) The water quality in the study area is clear, the marine sediment is mainly composed of coral debris and sand, and the seagrass plants are relatively tall. The Sum Green Index can effectively extract seagrass bed information and achieve good mapping accuracy. (ii) Between 1986 and 2021, there were significant changes in seagrass bed distribution in the study area, showing a trend of slow growth followed by rapid decline. Overall, over the past thirty years, the total area of seagrass beds in the study area has declined, coverage has decreased, and patches have become fragmented. (iii) The construction of marine and coastal engineering projects, as well as large-scale aquaculture, are important reasons for the decline of seagrass beds in the study area. Other types of mechanical physical damage, such as shipping and shells raking can also have serious impacts on seagrass beds. This study is the first to conduct distribution range mapping and long time series change monitoring based on satellite remote sensing images for the largest seagrass beds in China’s South China Sea Bioregion, with the aim of providing a reference for the monitoring and protection of seagrass beds in the South China Sea.

Spermosphere bacterial community at different germination stages of Ormosia henryi and its relationship with seed germination

Based on the important role of spermosphere microbes in seed germination, the driving factors of seed germination were explored under the influence of spermosphere bacteria by studying the time series changes of spermosphere bacterial communities in different germination stages of Ormosia henryi seeds. In this study, the soils under the mother trees of O. henryi in three places (Guanling, Qinglong, Shiqian in Guizhou, China) were used as the seed germination substrates to determine the spermosphere bacterial samples in different germination stages of Ormosia henryi seeds, and bioinformatics strategies were used to analyze bacterial diversity and its relationship with soil nutrients and germination rate, bacterial function prediction and microbial network characteristics. The results showed that seed germination rate of O. henryi was affected by both soil nutrients and abundance and species of spermosphere bacteria, most of the spermosphere bacteria that contributed the most to seed germination time had the functions of degradation and bioremediation. The spermosphere bacterial functions of respiratory metabolism, nodulation, degradation and energy were enhanced during seed germination. The spermosphere bacterial community reached a more stable state under the influence of germinating seeds during the stage of imbibition. This study revealed the contribution of spermosphere bacteria function, abundance and species to the germination rate and germination stage of O.henryi, providing a theoretical basis for screening and application of spermosphere functional bacteria.

Vegetation Trend Detection Using Time Series Satellite Data as Ecosystem Condition Indicators for Analysis in the Northwestern Highlands of Ethiopia

Vegetation is an essential component of the terrestrial ecosystem and has changed significantly over the last two decades in the Northwestern Highlands of Ethiopia. However, previous studies have focused on the detection of bitemporal change and lacked the incorporation of entire vegetation time series changes, which are considered significant indicators of ecosystem conditions. The Normalized Difference Vegetation Index (NDVI) time series dataset from the Moderate-Resolution Imaging Spectroradiometer (MODIS) is an efficient method for analyzing the dynamics of vegetation change over a lengthy period using remote sensing techniques. This study aimed to utilize time series satellite data to detect vegetation changes from 2000 to 2020 and investigate their links with ecosystem conditions. The time-series satellite processing package (TIMESAT) was used to estimate the seasonal parameter values of NDVI and their correlation across the seasons during the study period. Break Detection for Additive Season and Trend (BFAST) was applied to identify the year of breakpoints, the direction of magnitude, and the number of breakpoints. The results were reported, analyzed, and linked to ecosystem conditions. The overall trend in the study area increased from 0.58 (2000–2004) to 0.65 (2015–2020). As a result, ecosystem condition indicators such as peak value (PV), base value (BV), amplitude (Amp), and large integral (LI) exhibited significant positive trends, particularly for Acacia decurrens plantations, Eucalyptus plantations, and grasslands, but phenology indicator parameters such as start of season (SOS), end of season (EOS), and length of season (LOS) did not show significant trends for almost any vegetation type. The most abrupt changes were recorded in 2015 (24.7%), 2012 (18.6%), and 2014 (9.8%). Approximately 30% of the vegetation changes were positive in magnitude. The results of this study imply that there was an improvement in the ecosystem’s condition following the establishment of the Acacia decurrens plantation. The findings are considered relevant inputs for policymakers and serve as an initial stage for the assessment of the other environmental and climatic implications of Acacia decurrens plantations at the local scale.

Dynamic risk evaluation of hydrogen station leakage based on fuzzy dynamic Bayesian network

This paper introduces a dynamic risk assessment method for hydrogen leakage at hydrogen stations, employing fuzzy dynamic Bayesian networks. To begin, we utilize the Bow-Tie model for an in-depth analysis and consolidation of the primary risk factors contributing to hydrogen leak explosions. Subsequently, we establish a dynamic Bayesian network model for hydrogen leakage at hydrogen stations, adhering to the mapping rules derived from the Bow-Tie model. In order to reduce the impact of subjectivity, our model derives event prior probabilities through expert scoring and fuzzy set theory. Furthermore, the inclusion of a time factor is followed by the application of the Leaky Noisy-or gate model to enhance and calculate conditional probabilities, leading to the generation of time-series change curves for hydrogen leak probability and the probabilities associated with each accident consequence. The research findings yield valuable insights for risk assessment, accident prevention, and emergency management at hydrogen stations.

High-frequency sectoral carbon and environmental analysis based on monthly input-output tables compilation during 2018–2021

This paper proposes a process for updating monthly input-output tables with monthly macroeconomic statistics and published input-output tables. Reasonable assumptions are set up and 48monthly input-output tables are prepared from 2018 to 2021 with the combination of the row range series method and nonlinear mathematical planning. The Weaver-Thomas composite index is used to analyze the role of the sector in the economic network, and the sectoral correlation indicators are used to analyze the correlation change of the sector's monthly electricity emissions to show an environmental application effect of the monthly input-output table. The results show that the monthly input-output tables can be prepared with acceptable accuracy, and they can reveal the sectoral network structure changes and sectoral carbon emissions changes in continuous monthly time series. The proposed approach contributes for the compilation of high-time-frequency input-output tables, so as to support high-frequency industrial environmental impact analysis.

Realization of maximum flow in DTN and application in CGR

The maximum flow problem based on a contact graph in Delay-Tolerant Networking (DTN) is very important for routing and data planning. Common deterministic algorithms employing topological graphs with non-time varying to solve the maximum flow between two nodes include Dinic and Improved Shortest Augmenting Path (ISAP). However, these algorithms cannot be directly applied to topological networks with time series changes. Iosifidis.G gave a solution to this problem based on the time expansion graph, but his method requires high storage space, and an increase in the number of nodes results in increasing complexity. In this paper, we propose a method of dismantling and reconstructing the graph to solve the maximum flow problem in a continuously changing network. Compared with the Storage Policy (SP) algorithm of Iosifidis.G, this method does not require equal time slot splitting of each node, but instead uses discretization to reduce the scale of the graph. Finally, we add this algorithm to Contact Graph Routing (CGR) to optimize DTN data transmission, improve data delivery rate, and reduce unnecessary link occupation. Experimental results show that the optimized CGR can increase average delivery rate by 3% and reduce link usage by 4% compared to CGR.

Ratio Test for Mean Changes in Time Series with Heavy-Tailed AR(p) Noise Based on Multiple Sampling Methods

This paper discusses the problem of the mean changes in time series with heavy-tailed AR(p) noise. Firstly, it proposes a modified ratio-type test statistic, and the results show that under the null hypothesis of no mean change, the asymptotic distribution of the modified statistic is a functional of Lévy processes and the consistency under the alternative hypothesis is obtained. However, a heavy-tailed index exists in the asymptotic distribution and is difficult to estimate. This paper uses bootstrap sampling, jackknife sampling, and subsampling to approximate the distribution under the null hypothesis, and obtain more accurate critical values and empirical power. In addition, some results from a small simulation study and a practical example give an idea of the finite sample behavior of the proposed statistic.

Spatial analysis of urban expansion, land-use dynamics and its effects on land surface temperature in Oyo town, Southwestern Nigeria

This paper assessed the expansion of land use and land cover that occurred in Oyo State, Southwestern Nigeria, over a period of thirty-year. This was to assess urban expansion, landuse dynamics and their effects on land surface temperature in the area. The study used four multi-date imageries of the area. Landsat 7 ETM + , 1990, 2000, 2012, and Landsat 8 OLI/TIR, 2021 were collected from the United States Geological Survey (USGS) website were used. The ArcGIS 10.1 environment was utilised to clip the area of interest, and ERDAS Imagine remote sensing software was employed to efficiently improve and categorise the images. The images were then divided into five major: cultivation, built-up area, vegetation, water body, and bare soil using a maximum likelihood supervised classification algorithm. These categories were then utilised to conduct time series analysis or change detection analysis.Results of this study revealed that the built-up area has changed over the preceding three decades because of urban expansion. The built-up area increased from 19.83 km2 to 35.67 km2 in 2012 and 44.84 km2 in 2021, indicating increases of 14.93% to 79.87% and 25.71%, respectively, whereas the vegetative land cover decreased from -0.11% to -3.01 48% and 12.75% during the study period. Findings revealed that anthropogenic factors including urbanisation and agricultural expansion, have significantly altered the surface and atmospheric characteristics of the study area, causing a variety of heat patterns within the area. The built-up area had average temperatures between 35.05 °C and 36.16 °C between 1990 and 2021, which indicated a higher temperature with evidence of an urban heat island.This study concludes that the city’s land surface temperature is rising and that the expanding built-up area’s density and a corresponding decline in vegetation over time significantly impact it. Therefore, the study suggests cultivating and maintaining trees, promoting green environment,, public awareness, campaigns and education on actions that aggravate high temperatures are also required.