Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series

Matrix H-theory approach to stock market fluctuations

We introduce matrix H theory, a framework for analyzing collective behavior arising from multivariate stochastic processes with hierarchical structure. The theory models the joint distribution of the multiple variables (the measured signal) as a compound of a large-scale multivariate distribution with the distribution of a slowly fluctuating background. The background is characterized by a hierarchical stochastic evolution of internal degrees of freedom, representing the correlations between stocks at different timescales. As in its univariate version, the matrix H-theory formalism also has two universality classes, Wishart and inverse Wishart, enabling a concise description of both the background and the signal probability distributions in terms of Meijer G functions with matrix argument. Empirical analysis of daily returns of stocks within the S&P 500 demonstrates the effectiveness of matrix H theory in describing fluctuations in stock markets. These findings contribute to a deeper understanding of multivariate hierarchical processes and offer potential for developing more informed portfolio strategies in financial markets.

Influence of the Covid-19 pandemic on cerebrovascular diseases in the Sao Paulo region of Brazil

BACKGROUND

The rapid spread of covid-19 overwhelmed healthcare systems. This study aimed to investigate the impact of the covid-19 pandemic on hospitalizations and hospital deaths due to cerebrovascular diseases (CVD) in São Paulo state, Brazil.

METHODS

This ecologic study evaluated the CVD hospitalizations and hospital deaths (2017-2021) by demographic features and CVD type. During the pandemic (2020-2021), segmented regression models were used to detect changes in CVD trends. We also evaluated the detrended cross-correlation between CVD deaths and hospitalization with the SARS-Cov-2 infection series.

RESULTS

During the pandemic, there is a 35% reduction in CVD hospitalizations, mainly in elective admissions and ischemic stroke, but a 6.5% increase in deaths, especially in Black and Brown individuals, and those aged 20-29 years. From 2020 to 2021, Black and Brown individuals experience an earlier and more prolonged increase in hospital deaths. Ischemic CVD hospitalizations decrease in the first quarter of 2020. Older people exhibit a monthly increase of 2.9% in hospitalizations and 5.3% in deaths in the 2nd and 3rd quarters of 2021. SARS-Cov-2 infections are inversely correlated to CVD hospitalizations and directly correlated to CVD hospital deaths.

CONCLUSIONS

Covid-19 pandemic negatively affects CVD hospitalizations and deaths, particularly in Black and Brown individuals. The decrease in hospitalizations and increase in hospital deaths of ischemic CVD highlights vulnerability in accessing healthcare resources during the pandemic.

Multiscale detrended cross-correlation coefficient: estimating coupling in non-stationary neurophysiological signals

The brain consists of a vastly interconnected network of regions, the connectome. By estimating the statistical interdependence of neurophysiological time series, we can measure the functional connectivity (FC) of this connectome. Pearson's correlation (r P) is a common metric of coupling in FC studies. Yet r P does not account properly for the non-stationarity of the signals recorded in neuroimaging. In this study, we introduced a novel estimator of coupled dynamics termed multiscale detrended cross-correlation coefficient (MDC3). Firstly, we showed that MDC3 had higher accuracy compared to r P and lagged covariance using simulated time series with known coupling, as well as simulated functional magnetic resonance imaging (fMRI) signals with known underlying structural connectivity. Next, we computed functional brain networks based on empirical magnetoencephalography (MEG) and fMRI. We found that by using MDC3 we could construct networks of healthy populations with significantly different properties compared to r P networks. Based on our results, we believe that MDC3 is a valid alternative to r P that should be incorporated in future FC studies.

Cross-correlation and multifractality analysis of the Chinese and American stock markets based on the MF-DCCA model

Objective

To compare the multifractal features and factors of the Chinese and American stock markets and their correlation, complexity and uncertainty.

Methods

The paper analyzes the CSI 300 and S&P 500 indices from March 2018 to March 2023 using the MF-DCCA model and removes the long-term memory and nonlinear effects by random reshuffling and phase processing methods.

Results

The paper shows that (1) CSI 300 and S&P 500 have multifractal features, with different long-term memory, complexity and irregularity at different scales; (2) The markets are fractal movements influenced by investors' irrationality and expectations, not efficient markets; (3) Long-term memory and nonlinear effects cause the multifractal features. The paper offers a new perspective and method for the market investors and regulators.

Prioritizing cervical cancer candidate genes using chaos game and fractal-based time series approach

Cervical cancer is one of the most severe threats to women worldwide and holds fourth rank in lethality. It is estimated that 604, 127 cervical cancer cases have been reported in 2020 globally. With advancements in high throughput technologies and bioinformatics, several cervical candidate genes have been proposed for better therapeutic strategies. In this paper, we intend to prioritize the candidate genes that are involved in cervical cancer progression through a fractal time series-based cross-correlations approach. we apply the chaos game representation theory combining a two-dimensional multifractal detrended cross-correlations approach among the known and candidate genes involved in cervical cancer progression to prioritize the candidate genes. We obtained 16 candidate genes that showed cross-correlation with known cancer genes. Functional enrichment analysis of the candidate genes shows that they involve GO terms: biological processes, cell-cell junction assembly, cell-cell junction organization, regulation of cell shape, cortical actin cytoskeleton organization, and actomyosin structure organization. KEGG pathway analysis revealed genes' role in Rap1 signaling pathway, ErbB signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, Acute myeloid leukemia, chronic myeloid leukemia, Breast cancer, Thyroid cancer, Bladder cancer, and Gastric cancer. Further, we performed survival analysis and prioritized six genes CDH2, PAIP1, BRAF, EPB41L3, OSMR, and RUNX1 as potential candidate genes for cervical cancer that has a crucial role in tumor progression. We found that our study through this integrative approach an efficient tool and paved a new way to prioritize the candidate genes and these genes could be evaluated experimentally for potential validation. We suggest this may be useful in analyzing the nucleotide sequences and protein sequences for clustering, classification, class affiliation, etc.

Time-series-analysis-based detection of critical transitions in real-world non-autonomous systems

Real-world non-autonomous systems are open, out-of-equilibrium systems that evolve in and are driven by temporally varying environments. Such systems can show multiple timescale and transient dynamics together with transitions to very different and, at times, even disastrous dynamical regimes. Since such critical transitions disrupt the systems' intended or desired functionality, it is crucial to understand the underlying mechanisms, to identify precursors of such transitions, and to reliably detect them in time series of suitable system observables to enable forecasts. This review critically assesses the various steps of investigation involved in time-series-analysis-based detection of critical transitions in real-world non-autonomous systems: from the data recording to evaluating the reliability of offline and online detections. It will highlight pros and cons to stimulate further developments, which would be necessary to advance understanding and forecasting nonlinear behavior such as critical transitions in complex systems.

Mapping nationwide concentrations of sulfate and nitrate in ambient PM2.5 in South Korea using machine learning with ground observation data

Particulate matter (PM) is a major air pollutant in Northeast Asia, with frequent high PM episodes. To investigate the nationwide spatial distribution maps of PM2.5 and secondary inorganic aerosols in South Korea, prediction models for mapping SO42- and NO3- concentrations in PM2.5 were developed using machine learning with ground-based observation data. Specifically, the random forest algorithm was used in this study to predict the SO42- and NO3- concentrations at 548 air quality monitoring stations located within the representative radii of eight intensive air quality monitoring stations. The average concentrations of PM2.5, SO42-, and NO3- across the entire nation were 17.2 ± 2.8, 3.0 ± 0.6, and 3.4 ± 1.2 μg/m3, respectively. The spatial distributions of SO42- and NO3- concentrations in 2021 revealed elevated concentrations in both the western and central regions of South Korea. This result suggests that SO42- concentrations were primarily influenced by industrial activities rather than vehicle emissions, whereas NO3- concentrations were more associated with vehicle emissions. During a high PM2.5 event (November 19-21, 2021), the concentration of SO42- was primarily influenced by SOX emissions from China, while the concentration of NO3- was affected by NOX emissions from both China and Korea. The methodology developed in this study can be used to explore the chemical characteristics of PM2.5 with high spatiotemporal resolution. It can also provide valuable insights for the nationwide mitigation of secondary PM2.5 pollution.

Weighted Signed Networks Reveal Interactions between US Foreign Exchange Rates

Correlations between exchange rates are valuable for illuminating the dynamics of international trade and the financial dynamics of countries. This paper explores the changing interactions of the US foreign exchange market based on detrended cross-correlation analysis. First, we propose an objective way to choose a time scale parameter appropriate for comparing different samples by maximizing the summed magnitude of all DCCA coefficients. We then build weighted signed networks under this optimized time scale, which can clearly display the complex relationships between different exchange rates. Our study shows negative cross-correlations have become pyramidally rare in the past three decades. Both the number and strength of positive cross-correlations have grown, paralleling the increase in global interconnectivity. The balanced strong triads are identified subsequently after the network centrality analysis. Generally, while the strong development links revealed by foreign exchange have begun to spread to Asia since 2010, Europe is still the center of world finance, with the euro and Danish krone consistently maintaining the closest balanced development relationship. Finally, we propose a fluctuation propagation algorithm to investigate the propagation pattern of fluctuations in the inferred exchange rate networks. The results show that, over time, fluctuation propagation patterns have become simpler and more predictable.

Resting-state fractal brain connectivity is associated with impaired cognitive performance in healthy aging

Aging affects cognitive functions even in the absence of ongoing pathologies. The neurophysiological basis of age-related cognitive decline (CD), however, is not completely understood. Alterations in both functional brain connectivity and in the fractal scaling of neuronal dynamics have been linked to aging and cognitive performance. Recently, fractal connectivity (FrC) has been proposed - combining the two concepts - for capturing long-term interactions among brain regions. FrC was shown to be influenced by increased mental workload; however, no prior studies investigated how resting-state FrC relates to cognitive performance and plausible CD in healthy aging. We recruited 19 healthy elderly (HE) and 24 young control (YC) participants, who underwent resting-state electroencephalography (EEG) measurements and comprehensive cognitive evaluation using 7 tests of the Cambridge Neurophysiological Test Automated Battery. FrC networks were reconstructed from EEG data using the recently introduced multiple-resampling cross-spectral analysis (MRCSA). Elderly individuals could be characterized with increased response latency and reduced performance in 4-4 tasks, respectively, with both reaction time and accuracy being affected in two tasks. Auto- and cross-spectral exponents - characterizing regional fractal dynamics and FrC, respectively, - were found reduced in HE when compared to YC over most of the cortex. Additionally, fractal scaling of frontoparietal connections expressed an inverse relationship with task performance in visual memory and sustained attention domains in elderly, but not in young individuals. Our results confirm that the fractal nature of brain connectivity - as captured by MRCSA - is affected in healthy aging. Furthermore, FrC appears as a sensitive neurophysiological marker of age-related CD.

Fingerprints of decreased cognitive performance on fractal connectivity dynamics in healthy aging

Analysis of brain functional connectivity (FC) could provide insight in how and why cognitive functions decline even in healthy aging (HA). Despite FC being established as fluctuating over time even in the resting state (RS), dynamic functional connectivity (DFC) studies involving healthy elderly individuals and assessing how these patterns relate to cognitive performance are yet scarce. In our recent study we showed that fractal temporal scaling of functional connections in RS is not only reduced in HA, but also predicts increased response latency and reduced task solving accuracy. However, in that work we did not address changes in the dynamics of fractal connectivity (FrC) strength itself and its plausible relationship with mental capabilities. Therefore, here we analyzed RS electroencephalography recordings of the same subject cohort as previously, consisting of 24 young and 19 healthy elderly individuals, who also completed 7 different cognitive tasks after data collection. Dynamic fractal connectivity (dFrC) analysis was carried out via sliding-window detrended cross-correlation analysis (DCCA). A machine learning method based on recursive feature elimination was employed to select the subset of connections most discriminative between the two age groups, identifying 56 connections that allowed for classifying participants with an accuracy surpassing 92%. Mean of DCCA was found generally increased, while temporal variability of FrC decreased in the elderly when compared to the young group. Finally, dFrC indices expressed an elaborate pattern of associations-assessed via Spearman correlation-with cognitive performance scores in both groups, linking fractal connectivity strength and variance to increased response latency and reduced accuracy in the elderly population. Our results provide further support for the relevance of FrC dynamics in understanding age-related cognitive decline and might help to identify potential targets for future intervention strategies.

Reversal in the drought stress response of the Scots pine forest ecosystem: Local soil water regime as a key to improving climate change resilience

In a changing climate, forest ecosystems have become increasingly vulnerable to continuously exacerbating heat and associated drought conditions. Climate stress resilience is governed by a complex interplay of global, regional, and local factors, with hydrological conditions being among the key players. We studied a Scots pine (Pinus sylvestris L.) forest ecosystem located near the southern edge of the boreal ecotone, which is particularly subjected to frequent and prolonged droughts. By comparing the dendrochronological series of pines growing in apparently contrasting hydrological conditions ranging from the waterlogged peat bog area to the dry soil at the surrounding elevations, we investigated how the soil water regime affects the climate response and drought stress resilience of the forest ecosystem. We found that in the dry land area, a significant fraction of the trees were replaced after two major climate extremes: prolonged drought and extremely low winter temperatures. The latter has also been followed by a three- to ten-fold growth reduction of the trees that survived in the next year, whereas no similar effect has been observed in the peat bog area. Multi-scale detrended partial cross-correlation analysis (DPCCA) indicated that tree-ring width (TRW) was negatively correlated with spring and summer temperatures and positively correlated with the Palmer drought severity index (PDSI) for the same year. For the elevated dry land area, the above effect extends to interannual scales, indicating that prolonged heatwaves and associated droughts are among the factors that limit tree growth. In marked contrast, in the waterlogged peat bog area, a reversed tendency was observed, with prolonged dry periods as well as warmer springs and summers over several consecutive years, leading to increasing tree growth with a one- to three-year time lag. Altogether, our results indicate that the pessimal conditions of a warming climate could become favorable through the preservation of the soil water regime.

Networks analysis of Brazilian climate data based on the DCCA cross-correlation coefficient

Climate change is one of the most relevant challenges that the world has to deal with. Studies that aim to understand the behavior of environmental and atmospheric variables and the way they relate to each other can provide helpful insights into how the climate is changing. However, such studies are complex and rarely found in the literature, especially in dealing with data from the Brazilian territory. In this paper, we analyze four environmental and atmospheric variables, namely, wind speed, radiation, temperature, and humidity, measured in 27 Weather Stations (the capital of each of the 26 Brazilian states plus the federal district). We use the detrended fluctuation analysis to evaluate the statistical self-affinity of the time series, as well as the cross-correlation coefficient ρDCCA to quantify the long-range cross-correlation between stations, and a network analysis that considers the top 10% ρDCCA values to represent the cross-correlations between stations better. The methodology used in this paper represents a step forward in the field of hybrid methodologies, combining time series and network analysis that can be applied to other regions, other environmental variables, and also to other fields of research. The application results are of great importance to better understand the behavior of environmental and atmospheric variables in the Brazilian territory and to provide helpful insights about climate change and renewable energy production.

Wavelet gated multiformer for groundwater time series forecasting

Developing accurate models for groundwater control is paramount for planning and managing life-sustaining resources (water) from aquifer reservoirs. Significant progress has been made toward designing and employing deep-forecasting models to tackle the challenge of multivariate time-series forecasting. However, most models were initially taught only to optimize natural language processing and computer vision tasks. We propose the Wavelet Gated Multiformer, which combines the strength of a vanilla Transformer with the Wavelet Crossformer that employs inner wavelet cross-correlation blocks. The self-attention mechanism (Transformer) computes the relationship between inner time-series points, while the cross-correlation finds trending periodicity patterns. The multi-headed encoder is channeled through a mixing gate (linear combination) of sub-encoders (Transformer and Wavelet Crossformer) that output trending signatures to the decoder. This process improved the model's predictive capabilities, reducing Mean Absolute Error by 31.26 % compared to the second-best performing transformer-like models evaluated. We have also used the Multifractal Detrended Cross-Correlation Heatmaps (MF-DCCHM) to extract cyclical trends from pairs of stations across multifractal regimes by denoising the pair of signals with Daubechies wavelets. Our dataset was obtained from a network of eight wells for groundwater monitoring in Brazilian aquifers, six rainfall stations, eleven river flow stations, and three weather stations with atmospheric pressure, temperature, and humidity sensors.

Interplay of multifractal dynamics between shadow policy rates and stock markets

Stock markets are generally perceived as a barometer of the economy and respond to international monetary policies even before economic activities. Many central banks have turned to unconventional policy measures in response to various financial crises such as the global financial crisis of 2007-2009 or the recent crisis caused by COVID-19. To examine the cross-correlation of overall international monetary policies with stock markets, we employ the daily shadow short rate (SSR), which has the advantage of allowing comparison across unconventional and conventional regimes. The analysis is made through a multifractal context using multifractal detrended cross correlation analysis (MF-DXA), considering daily data from 1st January 2000 to 31st March 2022 and country specific SSR and the stock markets of eight developed economies. The main empirical findings are the following: (i) all the country specific pairs of SSR with stock markets have significant multifractal characteristics (ii) the pairs of NZ-SSR/NZX50, US-SSR/DJIA, and CN-SSR/S&P TSX have the highest multifractal patterns while EU-SSR/Euro-area Index has the lowest multifractal patterns (iii) Australian and New Zealand stock markets exhibit anti-persistent cross-correlation with SSR while the remainder have persistent cross-correlation in their multifractality. Lastly, the findings of this study have several important implications for central banks and stock market participants.

Affective Neural Responses Sonified through Labeled Correlation Alignment

Sound synthesis refers to the creation of original acoustic signals with broad applications in artistic innovation, such as music creation for games and videos. Nonetheless, machine learning architectures face numerous challenges when learning musical structures from arbitrary corpora. This issue involves adapting patterns borrowed from other contexts to a concrete composition objective. Using Labeled Correlation Alignment (LCA), we propose an approach to sonify neural responses to affective music-listening data, identifying the brain features that are most congruent with the simultaneously extracted auditory features. For dealing with inter/intra-subject variability, a combination of Phase Locking Value and Gaussian Functional Connectivity is employed. The proposed two-step LCA approach embraces a separate coupling stage of input features to a set of emotion label sets using Centered Kernel Alignment. This step is followed by canonical correlation analysis to select multimodal representations with higher relationships. LCA enables physiological explanation by adding a backward transformation to estimate the matching contribution of each extracted brain neural feature set. Correlation estimates and partition quality represent performance measures. The evaluation uses a Vector Quantized Variational AutoEncoder to create an acoustic envelope from the tested Affective Music-Listening database. Validation results demonstrate the ability of the developed LCA approach to generate low-level music based on neural activity elicited by emotions while maintaining the ability to distinguish between the acoustic outputs.

Analysis of the correlation between climatic variables and Dengue cases in the city of Alagoinhas/BA

The Aedes aegypti mosquito is the main vector of dengue and is a synanthropic insect and due to its anthropophilic nature, it has specific reproductive needs. In addition to that, it also needs tropical regions that provide climate-prone conditions that favor vector development. In this article, we propose the cross-correlation analysis between the climatic variables air temperature, relative humidity, weekly average precipitation and dengue cases in the period from 2017 to early 2021 in the municipality of Alagoinhas, Bahia, Brazil. To do so, we apply the trend-free cross-correlation, [Formula: see text], being a generalization of the fluctuation analysis without trend, where we calculate the cross correlation between time series to establish the influence of these variables on the occurrence of dengue disease. The results obtained here were a moderate correlation between relative humidity and the incidence of dengue cases, and a low correlation for relative air temperature and precipitation. However, the predominant factor in the incidence of dengue cases in the city of Alagoinhas is relative humidity and not air temperature and precipitation.

Response of cross-correlations between high PM2.5 and O3 with increasing time scales to the COVID-19: different trends in BTH and PRD

The air pollution in China currently is characterized by high fine particulate matter (PM2.5) and ozone (O3) concentrations. Compared with single high pollution events, such double high pollution (DHP) events (both PM2.5 and O3 are above the National Ambient Air Quality Standards (NAAQS)) pose a greater threat to public health and environment. In 2020, the outbreak of COVID-19 provided a special time window to further understand the cross-correlation between PM2.5 and O3. Based on this background, a novel detrended cross-correlation analysis (DCCA) based on maximum time series of variable time scales (VM-DCCA) method is established in this paper to compare the cross-correlation between high PM2.5 and O3 in Beijing-Tianjin-Heibei (BTH) and Pearl River Delta (PRD). At first, the results show that PM2.5 decreased while O3 increased in most cities due to the effect of COVID-19, and the increase in O3 is more significant in PRD than in BTH. Secondly, through DCCA, the results show that the PM2.5-O3 DCCA exponents α decrease by an average of 4.40% and 2.35% in BTH and PRD respectively during COVID-19 period compared with non-COVID-19 period. Further, through VM-DCCA, the results show that the PM2.5-O3 VM-DCCA exponents [Formula: see text] in PRD weaken rapidly with the increase of time scales, with decline range of about 23.53% and 22.90% during the non-COVID-19 period and COVID-19 period respectively at 28-h time scale. BTH is completely different. Without significant tendency, its [Formula: see text] is always higher than that in PRD at different time scales. Finally, we explain the above results with the self-organized criticality (SOC) theory. The impact of meteorological conditions and atmospheric oxidation capacity (AOC) variation during the COVID-19 period on SOC state are further discussed. The results show that the characteristics of cross-correlation between high PM2.5 and O3 are the manifestation of the SOC theory of atmospheric system. Relevant conclusions are important for the establishment of regionally targeted PM2.5-O3 DHP coordinated control strategies.

Correlation and causality between carbon and energy markets: a complexity perspective

Global warming obliges humans to focus on the relationships between carbon and energy markets. This study considers the relationship between carbon and energy markets from a complexity perspective. Chinese carbon prices and four energy indexes are selected as empirical variables. First, the complexities of carbon and energy markets are measured by multi-scale fuzzy entropy. The critical time delay of the series is then obtained by maximizing the Pearson coefficients between these markets. Further, analysis of the detrended cross-correlation coefficient confirms the existence of time delay. Finally, transfer entropy is applied to investigate the causality pertaining to dynamic complexity. Results of fuzzy entropy analysis reveal that the carbon market has lower complexity than energy markets. Meanwhile, multi-scale results indicate greater complexity on the small time scales in all markets than on the large time scales. The critical time delay is found to be about 50, which maximizes the correlation coefficient. Finally, causality between carbon and energy markets varies. Expectations in the carbon market impact oil gas and coal markets; electricity and new energy affect the carbon market; and cross-causality exists in the relationship between coal and carbon markets. The participants should focus on the information transmission between carbon and energy markets.

Detrended cross-correlations analysis between oil shocks and world food prices

Purpose

This paper aims to investigate the relationship between oil price shocks and world food prices between 1974 and 2018.

Design/methodology/approach

The authors use the SVAR model to disentangle the oil price into supply, aggregate demand and oil-specific demand shocks and apply the detrended cross-correlations analysis to measure the association between oil price shocks and food returns/volatility and analyze contagion effects between oil and food markets.

Findings

The results show that the correlations between oil and food prices depend on whether oil prices changes are driven by supply or demand shocks. Particularly, food returns (volatility) are positively (negatively) more dependent on the oil price changes driven by aggregate demand (oil specific demand) shocks. Further analysis dealing with contagion analysis between oil and food markets shows a contagion effect during the food crisis of 2006–2008. Oil-specific demand shocks are the main source of this phenomenon.

Research limitations/implications

This study differentiates itself from the previous literature by simultaneously disentangling oil price into supply, aggregate demand and oil-specific demand-driven shocks and evaluating the cross-correlations between each shock type and food returns/volatility. Specifically, this study has the originality of detecting the main source of contagion effects between oil and food markets over the food crisis of 2006–2008.

Practical implications

The results of this study are important for policymakers and investors. They should account for the oil price fluctuations differently depending on whether the oil price shocks are driven by the demand or supply side. Moreover, they should anticipate an increase (decrease) in food prices due to a positive (negative) oil shock. In addition, special attention should be accorded to the world oil demand. Finally, when a food crisis occurs, markets operators should focus more on the specific oil-demand shocks, as it is the most contributor to possible contagion effects between oil and food markets.

Originality/value

This study differentiates itself from the previous literature by simultaneously disentangling oil price into supply, aggregate demand and oil-specific demand-driven shocks and evaluating the cross-correlations between each shock type and food returns/volatility. Specifically, this study has the originality of detecting the main source of contagion effects between oil and food markets over the food crisis of 2006–2008.

Paradox between adequate sanitation and rainfall in dengue fever cases

BACKGROUND

Dengue fever is a tropical disease and a major public health concern, and almost half of the world's population lives in areas at risk of contracting this disease. Climate change is identified by WHO and other international health authorities as one of the primary factors that contribute to the rapid spread of dengue fever.

METHODS

We evaluated the effect of sanitation on the cross-correlation between rainfall and the first symptoms of dengue in the city of Mato Grosso do Sul, which is in a state in the Midwest region of Brazil, and employed the time-lagged detrended cross-correlation analysis (DCCAC) method.

RESULTS

Co-movements were obtained through the time-phased DCCAC to analyze the effects of climatic variables on arboviruses. The use of a time-lag analysis was more robust than DCCAC without lag to present the behavior of dengue cases in relation to accumulated precipitation. Our results show that the cross-correlation between rain and dengue increased as the city implemented actions to improve basic sanitation in the city.

CONCLUSION

With climate change and the increase in the global average temperature, mosquitoes are advancing beyond the tropics, and our results show that cities with improved sanitation have a high correlation between dengue and annual precipitation. Public prevention and control policies can be targeted according to the period of time and the degree of correlation calculated to structure vector control and prevention work in places where sanitation conditions are adequate.

On the inner dynamics between Fossil fuels and the carbon market: a combination of seasonal-trend decomposition and multifractal cross-correlation analysis

This study examines the inner dynamics of multifractality between the carbon market (EU ETS) and four major fossil fuel energy markets: Brent Crude Oil (BRN), Richards Bay Coal (RBC), UK Natural Gas (NGH2), and FTSE350 electricity index (FTSE350) from January 04, 2016, to March 04, 2022. First, we decompose the daily price changes by applying seasonal and trend decomposition using loess (STL). Then, we examine the inner dynamics of multifractality and cross-correlation by employing multifractal detrended fluctuation analysis (MFDFA) and multifractal detrended cross-correlation analysis (MFDCCA) using the remaining components of the return series. Our findings reveal that all series and the cross-correlations of carbon and fossil fuels markets have multifractal characteristics. We find crude oil to be the most efficient market (lowest multifractal), while coal is the least efficient (highest multifractal). Only coal shows persistent, whereas the other markets exhibit antipersistent behavior. Interestingly, the coal and EU ETS pair demonstrates a higher degree of multifractal patterns. In contrast, the pair of natural gas and EU ETS exhibits the lowest multifractal characteristics among the energy markets. Only the crude oil market shows persistent cross-correlations in the multifractality. These findings have important academic and managerial implications for investors and policymakers.

ECoalVis: Visual Analysis of Control Strategies in Coal-fired Power Plants

Improving the efficiency of coal-fired power plants has numerous benefits. The control strategy is one of the major factors affecting such efficiency. However, due to the complex and dynamic environment inside the power plants, it is hard to extract and evaluate control strategies and their cascading impact across massive sensors. Existing manual and data-driven approaches cannot well support the analysis of control strategies because these approaches are time-consuming and do not scale with the complexity of the power plant systems. Three challenges were identified: a) interactive extraction of control strategies from large-scale dynamic sensor data, b) intuitive visual representation of cascading impact among the sensors in a complex power plant system, and c) time-lag-aware analysis of the impact of control strategies on electricity generation efficiency. By collaborating with energy domain experts, we addressed these challenges with ECoalVis, a novel interactive system for experts to visually analyze the control strategies of coal-fired power plants extracted from historical sensor data. The effectiveness of the proposed system is evaluated with two usage scenarios on a real-world historical dataset and received positive feedback from experts.

Multi-fractal detrended cross-correlation heatmaps for time series analysis

Complex systems in biology, climatology, medicine, and economy hold emergent properties such as non-linearity, adaptation, and self-organization. These emergent attributes can derive from large-scale relationships, connections, and interactive behavior despite not being apparent from their isolated components. It is possible to better comprehend complex systems by analyzing cross-correlations between time series. However, the accumulation of non-linear processes induces multiscale structures, therefore, a spectrum of power-law exponents (the fractal dimension) and distinct cyclical patterns. We propose the Multifractal detrended cross-correlation heatmaps (MF-DCCHM) based on the DCCA cross-correlation coefficients with sliding boxes, a systematic approach capable of mapping the relationships between fluctuations of signals on different scales and regimes. The MF-DCCHM uses the integrated series of magnitudes, sliding boxes with sizes of up to 5% of the entire series, and an average of DCCA coefficients on top of the heatmaps for the local analysis. The heatmaps have shown the same cyclical frequencies from the spectral analysis across different multifractal regimes. Our dataset is composed of sales and inventory from the Brazilian automotive sector and macroeconomic descriptors, namely the Gross Domestic Product (GDP) per capita, Nominal Exchange Rate (NER), and the Nominal Interest Rate (NIR) from the Central Bank of Brazil. Our results indicate cross-correlated patterns that can be directly compared with the power-law spectra for multiple regimes. We have also identified cyclical patterns of high intensities that coincide with the Brazilian presidential elections. The MF-DCCHM uncovers non-explicit cyclic patterns, quantifies the relations of two non-stationary signals (noise effect removed), and has outstanding potential for mapping cross-regime patterns in multiple domains.

Scale-Free Functional Brain Networks Exhibit Increased Connectivity, Are More Integrated and Less Segregated in Patients with Parkinson's Disease following Dopaminergic Treatment

Dopaminergic treatment (DT), the standard therapy for Parkinson's disease (PD), alters the dynamics of functional brain networks at specific time scales. Here, we explore the scale-free functional connectivity (FC) in the PD population and how it is affected by DT. We analyzed the electroencephalogram of: (i) 15 PD patients during DT (ON) and after DT washout (OFF) and (ii) 16 healthy control individuals (HC). We estimated FC using bivariate focus-based multifractal analysis, which evaluated the long-term memory ( H ( 2 ) ) and multifractal strength ( Δ H 15 ) of the connections. Subsequent analysis yielded network metrics (node degree, clustering coefficient and path length) based on FC estimated by H ( 2 ) or Δ H 15 . Cognitive performance was assessed by the Mini Mental State Examination (MMSE) and the North American Adult Reading Test (NAART). The node degrees of the Δ H 15 networks were significantly higher in ON, compared to OFF and HC, while clustering coefficient and path length significantly decreased. No alterations were observed in the H ( 2 ) networks. Significant positive correlations were also found between the metrics of H ( 2 ) networks and NAART scores in the HC group. These results demonstrate that DT alters the multifractal coupled dynamics in the brain, warranting the investigation of scale-free FC in clinical and pharmacological studies.

Conservation in the Amazon rainforest and Google searches: A DCCA approach

In this paper we analyze the descriptive statistics of the Google search volume for the terms related to the National Reserve of Copper and Associates (RENCA), a Brazilian mineral reserve in the Amazon of 4.6 million hectares, before and after the government signed the decree releasing it for exploration. First, we analyze the volume of searches for expressions related to RENCA in Google Trends using descriptive statistics; second, we assess the cross-correlation coefficient ρDCCA, which measures the cross-correlation between two nonstationary time series across different time scales. After the government announced the release of the RENCA reserve, there was an increase in the average volume of Google searches for related terms, showing people's concern about the announcement. By using the cross-correlation coefficient ρDCCA, we identify strong cross-correlations between the different expressions related to RENCA in Google Trends. Our work shows the utility of Google Trends as an indicator of the perception of environmental policies. Additionally, we show that ρDCCA can be used as a tool to measure the cross-correlation between synonyms extracted from Google Trends for various time scales.

Effects of a body manipulation of Japanese martial arts on interpersonal correlation of postural sway

This study aimed to investigate the nature of a specific body manipulation named Suichoku-Ririku (SR) in Japanese martial arts. SR is regarded as a method to change the way of stance and to distort the balance control of the opponent, but its nature and mechanism are unknown. In the present study, we attempted to determine the effect of SR in the cases that a person stood alone (Expt. 1) and that two persons stood in contact (Expt. 2). We compared several center of pressure (COP) measures between the normal stance and SR stance conditions. When participants stood independently (Expt. 1), the COP path length, standard deviation of COP velocity and permutation entropy of the COP increased with the SR stance, which suggested that the SR maneuver destabilized a quiet stance. When two participants stood (with normal stance) in contact by wrist-holding or by a light touch (Expt. 2), their COP motions were correlated with each other, as previously reported. When one of the participants took the SR maneuver, their correlation and mutual information were maintained, denying the view that SR would diminish the interpersonal correlation of body sway. On the other hand, a fluctuation in the COP increased only for the participant taking the SR maneuver, and not for the other participant. This asymmetric effect of the SR maneuver between two participants, irrespective of maintained mutual correlation, suggest that the relationship between balance controls of two participants was partly disrupted. We discuss possible mechanisms for the present results.

Market Efficiency and Cross-Correlations of Chinese New Energy Market with Other Assets: Evidence from Multifractality Analysis

Despite the upgrading of the attention and investment of new energy in Chinese public, its market efficiency and associations with other assets are relatively rarely explored. This paper, firstly, explores the multifractal feature and market efficiency of Chinese new energy market (NEI) by the multifractal detrended fluctuation analysis. Secondly, the multifractal cross-correlation analysis is performed to discuss the multifractality of cross-correlations between NEI and crude oil, external new energy indices (Global (SPGCE), United States (ECO) and Europe (ERIX)) and safe-haven asset (GOLD) respectively. The results show that Chinese new energy market has obvious multifractality with low market efficiency, which is mainly sourced from long-range correlation. It has the strongest linkages with external new energy markets and most insignificant association with gold. The heterogeneous sources contribute to their multifractal cross-correlations. It provides useful enlightenment for decision-makers to implement energy policy and reform, and for investors to make investment decisions.

A spatiotemporal model of firearm ownership in the United States

Firearm injury is a major public health crisis in the United States, where more than 200 people sustain a nonfatal firearm injury and more than 100 people die from it every day. To formulate policy that minimizes firearm-related harms, legislators must have access to spatially resolved firearm possession rates. Here, we create a spatiotemporal econometric model that estimates monthly state-level firearm ownership from two cogent proxies (background checks per capita and fraction of suicides committed with a firearm). From calibration on yearly survey data that assess ownership, we find that both proxies have predictive value in estimation of firearm ownership and that interactions between states cannot be neglected. We demonstrate use of the model in the study of relationships between media coverage, mass shootings, and firearm ownership, uncovering causal associations that are masked by the use of the proxies individually.

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A spatiotemporal model of firearm prevalence in the United States is created

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The econometric model predicts firearm ownership in every state for every month

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Information theory is used to detail causal links related to firearm prevalence

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The media can influence firearm prevalence, which in turn moderates mass shootings

Firearm violence is a major public health crisis in the United States, where more than 200 people sustain a nonfatal firearm injury and more than 100 people die from it every day. Despite these unsettling figures, scientific research on firearm-related harm significantly lags behind because spatially and temporally resolved data on firearm ownership are unavailable. This paper presents a spatiotemporal model that predicts firearm prevalence at the resolutions of one state and one month from the numbers of background checks and suicides committed with a firearm. Drawing on principles from econometrics, the model also accounts for interactions between states. The model’s output is challenged in causal analysis, which uncovers unprecedented associations between firearm prevalence, media output on firearm regulations, and mass shootings.

Multifractal Cross-Correlations of Bitcoin and Ether Trading Characteristics in the Post-COVID-19 Time

Unlike price fluctuations, the temporal structure of cryptocurrency trading has seldom been a subject of systematic study. In order to fill this gap, we analyse detrended correlations of the price returns, the average number of trades in time unit, and the traded volume based on high-frequency data representing two major cryptocurrencies: bitcoin and ether. We apply the multifractal detrended cross-correlation analysis, which is considered the most reliable method for identifying nonlinear correlations in time series. We find that all the quantities considered in our study show an unambiguous multifractal structure from both the univariate (auto-correlation) and bivariate (cross-correlation) perspectives. We looked at the bitcoin–ether cross-correlations in simultaneously recorded signals, as well as in time-lagged signals, in which a time series for one of the cryptocurrencies is shifted with respect to the other. Such a shift suppresses the cross-correlations partially for short time scales, but does not remove them completely. We did not observe any qualitative asymmetry in the results for the two choices of a leading asset. The cross-correlations for the simultaneous and lagged time series became the same in magnitude for the sufficiently long scales.

Spatial Characteristics Analysis for Coupling Strength among Air Pollutants during a Severe Haze Period in Zhengzhou, China

This paper investigates the multifractal characteristics of six air pollutants using the coupling detrended fluctuation analysis method. The results show that coupling correlations exist among the air pollutants and have multifractal characteristics. The sources of multifractality are identified using the chi square test. The coupling strengths between different pollutants are quantified. In addition, the coupling contribution of a series in the haze system is calculated, and SO₂, as the main pollutant, plays a key role in the pollution system. Moreover, the Kriging interpolation method is used to analyze the spatial characteristic on coupling contribution of SO₂. The spatial analysis of coupling strength for air pollutants will provide an effective approach for pollution control.

Relative Prices of Ethanol-Gasoline in the Major Brazilian Capitals: An Analysis to Support Public Policies

The use of biomass as an energy source has advanced in recent decades, given the scientific evidence that it is a solution to the environmental problems faced globally. In this context, biofuels derived from biomass have a prominent role. Among the countries where this alternative is the most promising, Brazil stands out, just behind the USA. It is, therefore, necessary to assess whether such a replacement is economically viable. For such an assessment, the behavior of the relative price of bioethanol/gasoline is crucial. In the present work, the degree of temporal persistence of relative prices, considering the existence of shocks to which they are exposed, is evaluated, considering 15 important Brazilian capitals, via the detrended fluctuation analysis (DFA). The degree of correlation is also evaluated through the detrended cross-correlation analysis (DCCA) between fuel prices in São Paulo, the capital of the most populous state and main producer of bioethanol, with the capitals of the 14 states selected for the analysis. The period of analysis takes place between 2004 and 2020. The use of DCCA with sliding windows was recently proposed and we also evaluate DFA dynamically in this way, and this, together with an extended sample in the context of Brazilian fuel prices, represents the main innovations of the present work. We found that the degree of persistence varies significantly depending on the capitals analyzed, which means that price variations are localized and demand regional stimulus policies. Furthermore, it was found that the correlation with São Paulo is less intense in the most geographically distant capitals. Such evidence is important and complementary to infer how integrated the national bioethanol market is, in order to support public policies aimed at its consolidation.

The impact of COVID-19 pandemic on the dynamic correlations between gold and U.S. equities: evidence from multifractal cross-correlation analysis

This study exploits multifractal cross-correlation analysis (MFCCA) to investigate the impact of the COVID-19 pandemic on the cross-correlations between gold and U.S. equity markets using 1-min high-frequency data from January 1, 2019, to December 29, 2020. The MFCCA method shows that the pandemic caused an increase of multifractality in cross-correlations between the two markets. Specifically, the cross-correlations of small fluctuations became more persistent while those of large fluctuations became less persistent, explaining the source of multifractality. The findings of this study carry significant implications for investors, academicians, and policymakers. For example, the increase of multifractality of cross-correlation means that the non-linear relationship between gold and U.S. equity returns prevails more during economic downturns. Therefore, academicians may resort to non-linear techniques to evaluate the relationship between gold and U.S. equity markets during the health pandemic. Moreover, investors can know the value of hedging benefits over different investment time horizons during the pandemic. Finally, policymakers can better assess the economic downturns (i.e., those caused by health pandemics) over the dynamics of cross-correlation between gold and equity markets to make sound financial policies.

COVID-19 news and the US equity market interactions: An inspection through econometric and machine learning lens

This study investigates the impact of COVID-19 on the US equity market during the first wave of Coronavirus using a wide range of econometric and machine learning approaches. To this end, we use both daily data related to the US equity market sectors and data about the COVID-19 news over January 1, 2020-March 20, 2020. Accordingly, we show that at an early stage of the outbreak, global COVID-19s fears have impacted the US equity market even differently across sectors. Further, we also find that, as the pandemic gradually intensified its footprint in the US, local fears manifested by daily infections emerged more powerfully compared to its global counterpart in impairing the short-term dynamics of US equity markets.

A Denoising Method of Micro-Turbine Acoustic Pressure Signal Based on CEEMDAN and Improved Variable Step-Size NLMS Algorithm

The acoustic pressure signal generated by blades is one of the key indicators for condition monitoring and fault diagnosis in the field of turbines. Generally, the working conditions of the turbine are harsh, resulting in a large amount of interference and noise in the measured acoustic pressure signal. Therefore, denoising the acoustic pressure signal is the basis of the subsequent research. In this paper, a denoising method of micro-turbine acoustic pressure signal based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variable step-size Normalized Least Mean Square (VSS-NLMS) algorithms is proposed. Firstly, the CEEMDAN algorithm is used to decompose the original signal into multiple intrinsic mode functions (IMFs), based on the cross-correlation coefficient and continuous mean square error (CMSE) criterion; the obtained IMFs are divided into clear IMFs, noise-dominated IMFs, and noise IMFs. Finally, the improved VSS-NLMS algorithm is adopted to denoise the noise-dominated IMFs and combined with the clear IMF for reconstruction to obtain the final denoised signal. Adopting the above principles, the acoustic pressure signals generated by a micro-turbine with different rotation speeds and different states (normal turbine and fractured turbine) are denoised, respectively, and the results are compared with the axial flow fan test (ideal interference-free signal). The results show that the denoising method proposed in this paper has a good denoising effect, and the denoised signal is smooth and the important features are well preserved, which is conducive to the extraction of acoustic pressure signal characteristics.

An Online Unsupervised Dynamic Window Method to Track Repeating Patterns From Sensor Data

Short bursts of repeating patterns [intervals of recurrence (IoR)] manifest themselves in many applications, such as in the time-series data captured from an athlete's movements using a wearable sensor while performing exercises. We present an efficient, online, one-pass, and real-time algorithm for finding and tracking IoR in a time-series data stream. We provide a detailed theoretical analysis of the behavior of any IoR and derive fundamental properties that can be used on real-world data streams. We show that why our method, unlike current state-of-the-art techniques, is robust to variations in repeats of the same pattern adjacent to each other. To evaluate our algorithm, we build a wearable device that runs our algorithm to conduct a user study. Our results show that our algorithm can detect intervals of repeating activities on edge devices with high accuracy (over 70% F1 -Score) and in a real-time environment with only a 1.5-s lag. Our experimental results from real-world datasets demonstrate that our approach outperforms state-of-the-art algorithms in both accuracy and robustness to variations of the signal of recurrence.

Seasonal Variation and Spatial Heterogeneity of Water Quality Parameters in Lake Chenghai in Southwestern China

Seasonal dynamics and the vertical stratification of multiple parameters, including water temperature (WT), dissolved oxygen (DO), pH, and chlorophyll-a (Chl-a), were analyzed in Lake Chenghai, Northern Yunnan, based on monitoring data collected in 2015 (October), 2016 (March, May, July), 2017 (March, June, October), 2018 (August), and 2020 (June, November). The results indicate that the lake water was well mixed in winter and spring when the water quality was stable. However, when WT becomes stratified in summer and autumn, the Chl-a content and pH value changed substantially, along with the vertical movement of the thermocline. With rising temperature, the position of the stratified DO layer became higher than the thermocline, leading to a thickening of the water body with a low DO content. This process induced the release of nutrients from lake sediments and promoted eutrophication and cyanobacteria bloom. The thermal stratification structure had some influence on changes in DO, pH, and Chl-a, resulting in the obvious stratification of DO and pH. In summer, with an increase in temperature, thermal stratification was significant. DO and pH achieved peak values in the thermocline, and exhibited a decreasing trend from this peak, both upward and downward. The thermocline was anoxic and the pH value was low. Although Chl-a maintained a low level below the thermocline and was not high, there was a sudden increase in the surface layer, which should be urgently monitored to prevent large-scale algae reproduction and even local outbreaks in Lake Chenghai. Moreover, Lake Chenghai is deeper in the north and shallower in the south: this fact, together with the stronger wind–wave disturbance in the south, results in surface WT in the south being lower than that in the north year-round. This situation results in a gradual diminution of aquatic plants from north to south. Water quality in the lake’s southern extent is better than that in the north, exhibiting obvious spatial heterogeneity. It is recommended that lake water quality monitoring should be strengthened to more fully understand lake water quality and take steps to prevent further deterioration.

Why the parent's gaze is so powerful in organizing the infant's gaze: The relationship between parental referential cues and infant object looking

Parental scaffolding such as looking at and showing objects has long been considered to be helpful for early attention and language development. However, relatively little is known about how parental social multimodal cues work alone or together in guiding an infant's attention toward the referent items. The present study aims to document the dynamics of social referential input during an interactive play session and specify the different types of social cues in directing infant attention. Forty-three parent-infant dyads (infants aged from 5.0 to 18.0 months) in the U.S. completed a short play session recorded by head-mounted camera with eye-trackers. The present findings suggest that joint attention between parent and infant toward the same referent item often co-occurred with other referential input. Infants were more likely to maintain sustained attention to an object under the circumstance that the parent looked at the same item and named it explicitly. This was not the case when parent object looking accompanied other utterances, like "Look!" or the child's name. The present study highlights the importance of multimodal referential input, which sets up enriched opportunities for children to become sensitive to social input and develop sustained attention for further learning.

Application of Multifractal Analysis in Estimating the Reaction of Energy Markets to Geopolitical Acts and Threats

Since the industrial revolution, the geopolitics of energy has been a driver of global prosperity and security, and determines the survival of life on our planet. This study examines the nonlinear structure and multifractal behavior of the cross-correlation between geopolitical risk and energy markets (West Texas Intermediate (WTI), Brent, natural gas and heating oil), using the multifractal detrended cross-correlation analysis. Furthermore, an in-depth analysis reveals different associations of the indices of overall geopolitical risk, geopolitical acts, and geopolitical threats against the four energy products. Based on daily data ranging from 1 January 1985 to 30 August 2021, the findings confirm the presence of nonlinear dependencies, suggesting that geopolitical risk and energy markets are interlinked. Furthermore, significant multifractal characteristics are found and the degree of multifractality is stronger between the overall geopolitical risk and WTI while the lowest degree of multifractality is with Brent. Overall, for the WTI and heating-oil markets, the influence of geopolitical threats is more pronounced rather than their fulfilment. Contrarily, the Brent and natural gas are more correlated to geopolitical acts. Energy products exhibit heterogeneous persistence levels of cross-correlation with all the indicators of geopolitical risk, being more persistent in the case of small fluctuations compared to large fluctuations.

Design of a Bio-Inspired Gait Phase Decoder Based on Temporal Convolution Network Architecture With Contralateral Surface Electromyography Toward Hip Prosthesis Control

Inter-leg coordination is of great importance to guarantee the safety of the prostheses wearers, especially for the subjects at high amputation levels. The mainstream of current controllers for lower-limb prostheses is based on the next motion state estimation by the past motion signals at the prosthetic side, which lacks immediate responses and increases falling risks. A bio-inspired gait pattern generation architecture was proposed to provide a possible solution to the bilateral coordination issue. The artificial movement pattern generator (MPG) based on the temporal convolution network, fusing with the motion intention decoded from the surface electromyography (sEMG) measured at the impaired leg and the motion status from the kinematic modality of the prosthetic leg, can predict four sub gait phases. Experiment results suggested that the gait phase decoder exhibited a relatively high intra-subject consistency in the gait phase inference, adapted to various walking speeds with mean decoding accuracy ranging from 89.27 to 91.16% across subjects, and achieved an accuracy of 90.30% in estimating the gait phase of the prosthetic leg in the hip disarticulation amputee at the self-selected pace. With the proof of concept and the offline experiment results, the proposed architecture improves the walking coordination with prostheses for the amputees at hip level amputation.

Detrending-moving-average-based multivariate regression model for nonstationary series

Dependency between a response variable and the explanatory variables is a relationship of universal concern in various real-world problems. Multivariate linear regression (MLR) is a well-known method to focus on this issue. However, it is limited to dealing with stationary variables. In this work, we develop a MLR framework based on detrending moving average (DMA) analysis to reveal the actual dependency among variables with nonstationary measures. The DMA-based MLR can generate multiscale regression coefficients, which characterize different dependent behavior at different timescales. Artificial tests show that the DMA-MLR model can successfully resist the impact of trends on the studied series and produce more accurate regression coefficients with multiscale. Furthermore, some scale-dependent statistics are developed to deduce some important relationships in three typical DMA-based MLR models, which help us to deeply understand the DMA-MLR models in theory. The application of the proposed DMA-MLR framework to Beijing's air quality index system demonstrates that fine particulate matter with diameter ≤2.5μm (PM_{2.5}) is the dominant pollutant affecting the air quality of Beijing in recent years.

Instability of networks: effects of sampling frequency and extreme fluctuations in financial data

ABSTRACT

What determines the stability of networks inferred from dynamical behavior of a system? Internal and external shocks in a system can destabilize the topological properties of comovement networks. In real-world data, this creates a trade-off between identification of turbulent periods and the problem of high dimensionality. Longer time-series reduces the problem of high dimensionality, but suffers from mixing turbulent and non-turbulent periods. Shorter time-series can identify periods of turbulence more accurately, but introduces the problem of high dimensionality, so that the underlying linkages cannot be estimated precisely. In this paper, we exploit high-frequency multivariate financial data to analyze the origin of instability in the inferred networks during periods free from external disturbances. We show that the topological properties captured via centrality ordering is highly unstable even during such non-turbulent periods. Simulation results with multivariate Gaussian and fat-tailed stochastic process calibrated to financial data show that both sampling frequencies and the presence of outliers cause instability in the inferred network. We conclude that instability of network properties do not necessarily indicate systemic instability.

The Evaluation of Mean-Detrended Cross-Correlation Analysis Portfolio Strategy for Multiple risk Assets

The fractal characteristics of the security market were considered in portfolio strategy optimization. First, the detrended cross-correlation analysis was adopted to measure the fractal correlation of different securities. Second, the fractal correlation was embedded into the mean-variance criterion of the modern portfolio theory. Third, the mean-detrended cross-correlation analysis portfolio strategy of multiple risk assets was constructed, and the analytic solution of the strategy was given. Finally, the evaluation results revealed that the constructed the mean-detrended cross-correlation analysis portfolio strategy clearly improved investment performance, thus achieving the goal of optimizing the multiple risk asset portfolio strategy.

Permutation Jensen-Shannon distance: A versatile and fast symbolic tool for complex time-series analysis

The main motivation of this paper is to introduce the permutation Jensen-Shannon distance, a symbolic tool able to quantify the degree of similarity between two arbitrary time series. This quantifier results from the fusion of two concepts, the Jensen-Shannon divergence and the encoding scheme based on the sequential ordering of the elements in the data series. The versatility and robustness of this ordinal symbolic distance for characterizing and discriminating different dynamics are illustrated through several numerical and experimental applications. Results obtained allow us to be optimistic about its usefulness in the field of complex time-series analysis. Moreover, thanks to its simplicity, low computational cost, wide applicability, and less susceptibility to outliers and artifacts, this ordinal measure can efficiently handle large amounts of data and help to tackle the current big data challenges.

The spillover effects and connectedness among green commodities, Bitcoins, and US stock markets: Evidence from the quantile VAR network

Several economic and financial crises, as well as the recent health crisis, have driven major shock spillover channels over stock markets. While various empirical studies have explored risk transmission among financial markets, this study examined the shock spillover network across green markets, including the energy, solar, wind, water, and environment markets, as well as Bitcoins, uncertainty, and the US stock market. Indeed, this paper is the first study to ascertain whether green commodities, Bitcoins, and uncertainty are connected to the US stock market. Using the quantile vector autoregressive (VAR) connectedness framework, the key findings are as follows: (i) A static spillover network showed there was high spillover transfer between markets at extreme market states. (ii) Global green economy and global clean energy markets act as the highest contributors of information spillover under bearish market scenarios. (iii) With regard to dynamic connectedness, this work highlights the asymmetric spillover effect of green commodities, Bitcoins, and uncertainty on the US stock market. (iv) Bitcoin (BTC), uncertainty, and global carbon indexes were found to be net receipts of shock spillovers, while most green commodities acted as net contributors. (v) Significant implications for environmental and financial investors as well as policymakers are provided.

Real-Time Algorithm for Detrended Cross-Correlation Analysis of Long-Range Coupled Processes

Assessing power-law cross-correlations between a pair - or among a set - of processes is of great significance in diverse fields of analyses ranging from neuroscience to financial markets. In most cases such analyses are computationally expensive and thus carried out offline once the entire signal is obtained. However, many applications - such as mental state monitoring or financial forecasting - call for fast algorithms capable of estimating scale-free coupling in real time. Detrended cross-correlation analysis (DCCA), a generalization of the detrended fluctuation analysis (DFA) to the bivariate domain, has been introduced as a method designed to quantify power-law cross-correlations between a pair of non-stationary signals. Later, in analogy with the Pearson cross-correlation coefficient, DCCA was adapted to the detrended cross-correlation coefficient (DCCC), however as of now no online algorithms were provided for either of these analysis techniques. Here we introduce a new formula for obtaining the scaling functions in real time for DCCA. Moreover, the formula can be generalized via matrix notation to obtain the scaling relationship between not only a pair of signals, but also all possible pairs among a set of signals at the same time. This includes parallel estimation of the DFA scaling function of each individual process as well, thus allowing also for real-time acquisition of DCCC. The proposed algorithm matches its offline variants in precision, while being substantially more efficient in terms of execution time. We demonstrate that the method can be utilized for mental state monitoring on multi-channel electroencephalographic recordings obtained in eyes-closed and eyes-open resting conditions.

Functional Connectivity Methods and Their Applications in fMRI Data

The availability of powerful non-invasive neuroimaging techniques has given rise to various studies that aim to map the human brain. These studies focus on not only finding brain activation signatures but also on understanding the overall organization of functional communication in the brain network. Based on the principle that distinct brain regions are functionally connected and continuously share information with each other, various approaches to finding these functional networks have been proposed in the literature. In this paper, we present an overview of the most common methods to estimate and characterize functional connectivity in fMRI data. We illustrate these methodologies with resting-state functional MRI data from the Human Connectome Project, providing details of their implementation and insights on the interpretations of the results. We aim to guide researchers that are new to the field of neuroimaging by providing the necessary tools to estimate and characterize brain circuitry.

Multiple-Resampling Cross-Spectral Analysis: An Unbiased Tool for Estimating Fractal Connectivity With an Application to Neurophysiological Signals

Investigating scale-free (i.e., fractal) functional connectivity in the brain has recently attracted increasing attention. Although numerous methods have been developed to assess the fractal nature of functional coupling, these typically ignore that neurophysiological signals are assemblies of broadband, arrhythmic activities as well as oscillatory activities at characteristic frequencies such as the alpha waves. While contribution of such rhythmic components may bias estimates of fractal connectivity, they are also likely to represent neural activity and coupling emerging from distinct mechanisms. Irregular-resampling auto-spectral analysis (IRASA) was recently introduced as a tool to separate fractal and oscillatory components in the power spectrum of neurophysiological signals by statistically summarizing the power spectra obtained when resampling the original signal by several non-integer factors. Here we introduce multiple-resampling cross-spectral analysis (MRCSA) as an extension of IRASA from the univariate to the bivariate case, namely, to separate the fractal component of the cross-spectrum between two simultaneously recorded neural signals by applying the same principle. MRCSA does not only provide a theoretically unbiased estimate of the fractal cross-spectrum (and thus its spectral exponent) but also allows for computing the proportion of scale-free coupling between brain regions. As a demonstration, we apply MRCSA to human electroencephalographic recordings obtained in a word generation paradigm. We show that the cross-spectral exponent as well as the proportion of fractal coupling increases almost uniformly over the cortex during the rest-task transition, likely reflecting neural desynchronization. Our results indicate that MRCSA can be a valuable tool for scale-free connectivity studies in characterizing various cognitive states, while it also can be generalized to other applications outside the field of neuroscience.

Herding behavior during the Covid-19 pandemic: a comparison between Asian and European stock markets based on intraday multifractality

With the spread of Covid-19, investors’ expectations changed during 2020, as well as financial markets’ policy responses and the structure of global financial intermediation itself. These dynamics are studied in this paper, which analyzes quarterly changes in herding behavior by quantifying the self-similarity intensity of six stock markets in Asia and Europe. A multifractal detrended fluctuation analysis (MFDFA) is applied, using intraday trade prices with a 15-min frequency from Jan-2020 to Dec-2020. The empirical results confirm that Covid-19 had a significant impact on the efficiency of the stock markets under study, although with a quarterly varying impact. During the first quarter of the year, European stock markets remained efficient compared to Asian markets; in the subsequent two quarters, the Chinese stock market showed significant improvement in its efficiency and became the least inefficient market, with a decline in the market efficiency of the UK and Japan. Furthermore, European markets are more sensitive to asset losses than Asian markets, so investors are more likely to show herding in the former. Herding was at its peak during the 2nd quarter of 2020. These findings could be related to possible market inefficiencies and herding behavior, implying the possibility of investors forming profitable trading strategies.