Short-term Correlation Research Articles

Forex market forecasting with two-layer stacked Long Short-Term Memory neural network (LSTM) and correlation analysis

Since it is one of the world's most significant financial markets, the foreign exchange (Forex) market has attracted a large number of investors. Accurately anticipating the forex trend has remained a popular but difficult issue to aid Forex traders' trading decisions. It is always a question of how precise a Forex prediction can be because of the market's tremendous complexity. The fast advancement of machine learning in recent decades has allowed artificial neural networks to be effectively adapted to several areas, including the Forex market. As a result, a slew of research articles aimed at improving the accuracy of currency forecasting has been released. The Long Short-Term Memory (LSTM) neural network, which is a special kind of artificial neural network developed exclusively for time series data analysis, is frequently used. Due to its high learning capacity, the LSTM neural network is increasingly being utilized to predict advanced Forex trading based on previous data. This model, on the other hand, can be improved by stacking it. The goal of this study is to choose a dataset using the Hurst exponent, then use a two-layer stacked Long Short-Term Memory (TLS-LSTM) neural network to forecast the trend and conduct a correlation analysis. The Hurst exponent (h) was used to determine the predictability of the Australian Dollar and United States Dollar (AUD/USD) dataset. TLS-LSTM algorithm is presented to improve the accuracy of Forex trend prediction of Australian Dollar and United States Dollar (AUD/USD). A correlation study was performed between the AUD/USD, the Euro and the Australian Dollar (EUR/AUD), and the Australian Dollar and the Japanese Yen (AUD/JPY) to see how AUD/USD movement affects EUR/AUD and AUD/JPY. The model was compared with Single-Layer Long Short-Term (SL-LSTM), Multilayer Perceptron (MLP), and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Improved Firefly Algorithm Long Short-Term Memory. Based on the evaluation metrics Mean Square Error (MSE), Root Mean Square Error, and Mean Absolute Error, the suggested TLS-LSTM, whose data selection is based on the Hurst exponent (h) value of 0.6026, outperforms SL-LSTM, MLP, and CEEMDAN-IFALSTM. The correlation analysis conducted shows both positive and negative relations between AUD/USD, EUR/AUD, and AUD/JPY which means that a change in AUD/USD will affect EUR/AUD and AUD/JPY as recorded depending on the magnitude of the correlation coefficient (r).

The Effects of Homeownership on Wealth Distribution

Within the broader context of new dimensions of poverty such as housing poverty, energy poverty, etc., this article describes dependencies between household income, real estate ownership and socio-economic trends. We argue that income is not the principal determinant for home ownership rate, but rather recent lifestyle changes can better explain the homeownership decreasing trend in developed economies. Job mobility, family formation determinants and demographical trends seem to find well-supported basis in literature and data. Using data for the US states we have proved that the decreasing rate of home ownership may be explained by social aspects of changing lifestyle such as increasing share of population moving from rural areas to cities, age of marriage, divorce rate, career-oriented lifestyle, rather than by the frequently cited price-income ratio. We have also observed a short-term correlation between financing availability and homeownership rate, but we conclude that property prices would adjust to lose monetary policy without any long-term effect on homeownership rate. It results that government or monetary policies aimed to cushion the housing unavailability (recently increasing value of price-income) ratio may distort the housing market. We propose a new insight in the housing availability discussion.

YENİLENEBİLİR ENERJİ TÜKETİMİ, FİNANSAL GELİŞMİŞLİK VE EKONOMİK BÜYÜME ARASINDAKİ İLİŞKİ: IEA ÜLKELERİ ÜZERİNE UYGULAMA

The need for energy sources is increasing day by day. However, limited energy resources lead economies to use renewable energy resources. This paper explores the relations between renewable energy consumption, financial development, and economic growth. The study employs ARDL approach to investigate the long-term and short-term cointegration correlations between variables. The current paper focuses on the period 1996-2017 to investigate whether financial development indicators can play a role on renewable energy consumption for twelve IEA countries. According to panel cointegration test results, long-term variables are statistically significant. Also economic growth is observed affecting renewable energy consumption positively in the short term. According to panel causality test results in the short-term there is relation between financial development and renewable energy consumption, and economic growth and renewable energy consumption. Finally, the use of subcomponents of financial development indicator have efficiently captured the different aspects of financial system affecting renewable energy consumption.

Pengaruh Sektor Ekonomi Perbankan terhadap Pembentukan Modal di Indonesia

This research aimed to know the credit and economic sector financing of banking economy effect on capital formation in Indonesia. The more investments that banks receive from investors, the more profits they will get in the future. In this case capital formation can develop. The research type is descriptive quantitative research. This research involved Islamic Commercial Bank, Islamic Business Unit, Conventional Banking and used monthly data in 2015-2020. This research used Autoregressive Distributed Lag (ARDL) to verify short term or long term correlation of independent variables on capital formation in Indonesia. The results of this research show that the short term variables of the agricultural sector and the manufacturing sector have no effect on capital formation. While, foreign direct investment has an effect on capital formation in Indonesia. Then in the long term, the agricultural sector has not effect on capital formation. While, the manufacturing sector and foreign direct investment have a direct effect on capital formation in Indonesia. Therefore recommended that the monetary authority make efforts effectively manage bank maximal loans.

The impact of COVID‐19 on the interdependence between US and Chinese oil futures markets

The oil futures market plays a vital role in the global financial system, especially after the negative future oil price rose during the COVID‐19 pandemic. This paper investigates the COVID‐19 impact on the interdependence between the US and Chinese oil futures markets by extending the dynamic conditional correlation‐generalized autoregressive conditional heteroskedasticity (DCC‐GARCH) models with incorporating COVID‐19 variables and by applying vector autoregression (VAR) models. Our study reveals that the COVID‐19 pandemic enhanced the long‐run correlation between the two oil markets. In contrast, daily changes in pandemic severity had a negative effect on the short‐term transient correlation. Our results show that COVID‐19 changed the one‐direction causality from the US oil market to the Chinese market in the pre‐COVID period to a bidirectional causal relation between the two markets during the COVID period. It strengthened the volatility spillover effect from the Chinese to US markets. These findings are helpful to regulars' monitoring oil supply chain risk and investors' cross‐market hedging of spillover risks from a systematic risk perspective.

Layered Convolutive Nonnegative Matrix Factorization for Speech Separation

Abstract Nonnegative matrix factorization (NMF) has attracted significant attention for its good performance in single-channel speech separation. The improved algorithms of NMF have become research hotspots. Layered NMF (LNMF), an improved algorithm, can express the source signal more accurately for its multilayer structure. However, LNMF sometimes performs poorly because it ignores the short-term correlation of speech signals. Based on LNMF and the advantages of Convolutive NMF (CNMF), we proposed a Layered Convolutive NMF(LCNMF) algorithm for single-channel speech separation. The LCNMF corporates the multilayer structure into the NMF and expands the convolution of the top-level NMF model. During the training, NMF is used to learn the non-top-level basis matrices, and CNMF is used to learn the top-level basis matrix, then combined with each single-layer of basis matrix. During the prediction, CNMF is used to separate mixed signals. The results on the dataset MIK-1K showed that LCNMF outperformed NMF and LNMF for separating the mixture of single-channel speech signals. LCNMF improved by 0.019, 1.049dB, 1.305dB, and 0.851dB on average compared with NMF, and improved by 0.007, 0.172dB, 0.090dB, and 0.366dB on average compared with LNMF in sort-term objective intelligibility (STOI), Source to Distortion Ratio (SDR), Source to Interference Ratio (SIR) and Source to Artifacts Ratio (SAR)

Altmetric and bibliometric analysis of obstetrics and gynecology research: influence of public engagement on citation potential

Whether research engagement on social media and other public platforms results in increased citations in obstetrics and gynecology remains uncertain. The Altmetric Attention Score is a metric of research influence based on mentions on social media and public platforms, such as newsfeeds and Wikipedia. The correlation between Altmetric Attention Scores, absolute citation rates, and the Relative Citation Ratio (a novel metric of research engagement also based on citation rates) in obstetrics and gynecology research is uncertain. To evaluate the correlation between Altmetric Attention Score, absolute citation rate, and Relative Citation Ratio for articles published in obstetrics and gynecology journals from 2004 to 2019. Our second objective was to identify, characterize, and compare the 100 articles with highest Altmetric Attention Scores, the 100 most-cited articles, and the 100 articles with highest Relative Citation Ratios. We performed a cross-sectional altmetric and bibliometric study of all obstetrics and gynecology articles indexed in the National Institutes of Health Open Citation Collection from 2004 to 2019. Articles were included if they were published in obstetrics and gynecology journals according to InCites Journal Citation Reports indexing. Citation data, including citation numbers and Relative Citation Ratios, were downloaded on March 20, 2021 and merged with altmetric data from the Altmetric Explorer on the basis of each article's unique PubMed identification number. We assessed correlation between Altmetric Attention Scores and number of citations and Altmetric Attention Scores and Relative Citation Ratios by calculating the Pearson correlation coefficient. The 100 articles with highest Altmetric Attention Scores, the 100 most-cited articles, and the 100 articles with highest Relative Citation Ratios were characterized and compared using means (standard deviations) and mean differences (95% confidence intervals). There were 156,592 articles published in 82 obstetrics and gynecology journals and indexed in the National Institutes of Health Open Citation Collection between 2004 and 2019. The correlation coefficient was 0.18 (95% confidence interval, 0.17-0.19) for Altmetric Attention Scores vs number of citations and 0.10 (95% confidence interval, 0.09-0.11) for Altmetric Attention Scores vs Relative Citation Ratios. There was no overlap among the 100 articles on the highest Altmetric Attention Score list and the 100 most-cited list, and there was minimal overlap among the 100 articles on the highest Altmetric Attention Score list and the 100 highest Relative Citation Ratio list (98 unique articles on each list). Articles with highest Altmetric Attention Scores generated substantially more engagement on social media and other public platforms than most-cited articles (mean Altmetric Attention Score, 763.1 [standard deviation, 520.8] vs 49.9 [standard deviation, 81.6]; mean difference,-713.2 [95% confidence interval,-819.9 to -606.6]) and highest Relative Citation Ratio articles (mean, 116.2 [standard deviation, 415.9]; mean difference,-661.5 [95% confidence interval,-746.2 to-576.9]). In contrast, the articles with highest Altmetric Attention Scores generated far fewer citations than most-cited articles (mean, 39.7 [standard deviation, 47.6] vs 541.8 [standard deviation, 312.8]; mean difference, 502.0 [95% confidence interval, 439.0-565.0]) and highest Relative Citation Ratio articles (mean, 458.9 [standard deviation, 363.5]; mean difference, 427.7 [95% confidence interval, 353.8-501.6]). Nearly half of articles with highest Altmetric Attention Scores were basic/translational studies, often about menopause and environmental factors impacting fertility, whereas most-cited articles and articles with highest Relative Citation Ratios were more likely to be reviews and consensus statements, respectively, often about placentation and polycystic ovary syndrome, respectively. Articles with highest Altmetric Attention Scores were more likely to be published as open-access. There seems to be weak short-term correlation between Altmetric Attention Scores and citation rates. Further study is warranted to ascertain whether there may be long-term correlation between alternative metrics and citation rates in obstetrics and gynecology.

The Role of Digital Financial Inclusion and Social Welfare in Realizing Sustainable Development

The goal of this study is to determine how social welfare and digital financial inclusion relate to one another and to how they affect sustainable development in Malaysia. We take data from the World Bank as a secondary source for the years 2000 to 2020, From our estimation results, We find that the variables we estimate have long-term and short-term correlations such as economic growth and digital financial inclusion and economic growth with life expectancy, in the short term digital financial inclusion has a negative effect on economic growth, then life expectancy also has a significant negative relationship with economic growth, but in the long term, it is significantly positive. This shows that between the two variables, with increasing economic growth, the number of digital financial inclusion will actually decrease, although this only applies in the short term, then in the long term with increasing life expectations will increase economic growth in Malaysia.

Green Economy and Social Welfare in Malaysia: ARDL Approach

This study tries to ascertain how certain aspects of the green economy perspective relate to Malaysian economic growth using the variables of CO2 emissions, renewable energy consumption, and life expectancy which are representations of social welfare in Malaysia. We take data from the World Bank as a secondary source for the years 2000 to 2020, From our estimation results, We find that the variables we estimate have long-term and short-term correlations such as economic growth and life expectancy and economic growth with renewable energy consumption, and short-term economic expansion has a detrimental impact on life expectancy. However, the use of renewable energy is significantly correlated with economic expansion. This shows that between the two variables, with increasing economic growth, life expectancy will actually decrease, although this occurs in the short term, furthermore with increasing consumption of renewable energy will also increase economic growth in Malaysia.

The Role of Technology and Investment in Digitalization and The Economy in Malaysia

This study aims to determine the role of technology, especially the internet, and investment trends in the non-financial sector in the development of the Malaysian economy. We take data from the World Bank as a secondary source for the years 2000 to 2020, From our estimation results, We find that the variables we estimate have long-term and short-term correlations such as the variable economic growth with the development of internet users and economic growth with investment, in the short term an increase in the development of internet users will have a positive effect on economic growth, but in the long run, the opposite is true. , it is different with investment and economic growth variables, in the short term this variable will have a negative impact on economic growth, but in the long term, an increase in investment will greatly affect the performance of economic growth. This shows that in the long term, digitalization of the economy is still a significant step in projecting economic growth in Malaysia.

Green Economy and Sustainable Development in Indonesia: ARDL Approach

This study attempts to determine how the link between air pollution, economic growth, carbon dioxide damage, and life expectancy in Indonesia reflects the long-term and short-term effects of factors connected to the green economy and sustainable development. We take data from the World Bank as a secondary source for the years 2000 to 2020, From our estimation results, we find that the variables we estimate have long-term and short-term correlations such as air pollution and economic growth and economic growth with economic growth in the previous year, in the short term air pollution has a negative effect on economic growth, as well as economic growth in 2016. previously. In contrast to the relationship between the variable economic growth and life expectancy, which is significantly positive, this shows that between the two with increasing economic growth, life expectancy will also increase. The variable of carbon dioxide damage has a positive and insignificant relationship with economic growth.

The role of serum HLD, LDL, and uric acid levels in predicting the prognosis of community-acquired pneumonia

Purpose: Community-acquired pneumonia (CAP) leads to inflammation and oxidative stress as a result in infection. In the present study, we aimed to investigate the relationship between initial serum high-density lipoprotein (HDL), low-density lipoprotein (HDL), and uric acid levels on short-term (30-day) changes in the course of CAP. Materials and methods: The retrospective study included 113 patients with CAP that received inpatient care between 2012 and 2018.The HDL, LDL and uric acid values in the blood received in hospital admission were examined for their relationship with short-term mortality and correlation with hospitalization. Results: The 113 patients comprised 71 (62.8%) men and 42 (37.2%) women with a mean age of 74±11 years. Of the 113 patients, 30-day mortality occurred in 12 (10.6%) patients. Of the 113 patients, 17 (16.5%) patients were hospitalized in ICU. All cases detected as mortality were followed in ICU. Normal ranges of HDL, LDL, and uric acid concentrations were accepted as 40-70 mg/dL, 60-130 mg/dL, and 1.8-8 mg/dL, respectively. The mean serum LDL, HDL, and uric acid levels were 97.95±42.11 (range, 26-271) mg/dL, 40.94±13.70 (range, 5.10-83.20) mg/dL, and 5.99±2.18 (range, 1.40-13.20) mg/dL, respectively. HDL and LDL levels were found to be insignificant predictors of 30-day mortality ( p =0.482 and p =0.725, respectively). Similarly, serum HDL, LDL, and uric acid levels were found to be insignificant predictors of the duration of hospitalization and ICU requirement. On the other hand, multivariate logistic regression analysis indicated that uric acid concentration was a protective factor for 30-day mortality (OR, 0.694; 95% Cl, 0.478-0.989, p =0.048), whereby increased uric acid concentration was found to decrease the risk of mortality and reduced uric acid concentration was found to increase the risk of mortality by 1.44 times (1/0.694). Conclusion: We suggest that monitoring uric acid levels with serial measurements in patients with CAP may be useful for predicting the short term prognosis. Further prospective multicentric studies with larger patient series are needed to investigate the correlation between serum uric acid levels and clinical risk scores.

Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea

As an area frequently suffering from storm surge, the Yangtze River Estuary in the East China Sea requires fast and accurate prediction of water level for disaster prevention and mitigation. Due to storm surge process being affected by the long-term and short-term correlation of multiple factors, this study attempts to introduce a data-driven idea into the water level prediction during storm surge. By collecting the observed meteorological data and water level data of 12 typhoons from 1986 to 2016 at the Lusi tidal station of Jiangsu Province, China near the north branch of the Yangtze River Estuary, a Long Short-Term Memory (LSTM) neural network model was constructed by using multi-factor time series to predict the water level during the storm surge period. This study concludes that the LSTM model performs precisely for 1 h prediction of water level during the storm surge period and it can provide a 15 h prediction of water level within a limited error, and the prediction performance of the LSTM model is visibly superior to the four traditional ML models by 41% in terms of Accuracy Coefficient.

Carbon Emissions and Brazilian Ethanol Prices: Are They Correlated? An Econophysics Study

Brazil is one of the largest global producers and exporters of ethanol and in 2017 launched RenovaBio, a programme aiming to mitigate greenhouse gas emissions. In parallel to this domestic scenario, there is rapid growth in the world market of carbon production, as well as complex price relations between fossil and renewable energies becoming increasingly important in recent years. The present work aims to contribute to filling a gap in knowledge about the relationship between Brazilian ethanol and other relevant energy-related commodities. We use a recent methodology (Detrended Cross-Correlation Approach—DCCA—with sliding windows) to analyze dynamically the cross-correlation levels between Brazilian ethanol prices and carbon emissions, as well as other possible-related prices, namely: sugar, Brent oil, and natural gas prices, with a sample of daily prices between January 2010 and July 2020. Our results indicate that (i) in the whole period, Brazilian ethanol has significant correlations with sugar, moderate correlation with oil in the short term, and only a weak, short-term correlation with carbon emission prices; (ii) with a sliding windows approach, the strength of the correlation between ethanol and carbon emissions varies between weak and non-significant in the short term.

Relationship among Economic Growth (GDP), Energy Consumption and Carbon Dioxide Emission: Evidence from V4 Countries

The main objective of the research was to determine the long-term and short-term correlation between CO2 emissions per capita, energy consumption per capita, and the level of economic growth of GDP per capita in the V4 countries. These countries, being EU members since 2004 and previously being in the sphere of influence of the Soviet Union, have introduced a number of economic reforms, but in the area of climate protection, including decarbonization, they struggle with many problems, as their economies are largely dependent on energy from non-renewable resources. The results of the research are varied, but the authors confirmed, especially in the case of Poland, the long-term correlations between the studied variables. In the short term, such interdependencies also occurred, especially between the level of energy consumption per capita and the level of CO2 emissions.

Music Segmentation Algorithm Based on Self-Adaptive Update of Confidence Measure

To improve the accuracy of music segmentation and enhance segmentation effect, an algorithm based on the adaptive update of confidence measure is proposed. According to the theory of compressed sensing, the music fragments are denoised, and thus the denoised signals are subjected to short-term correlation analysis. Then, the pitch frequency is extracted, and the music fragments are roughly classified by wavelet transform to realize the preprocessing of the music fragments. In order to calculate the confidence measure of the music segment, the SVM method is used, whereas the adaptive update of the confidence measure is studied using reliable data selection algorithm. The dynamic threshold notes are segmented according to the update result to realize music segmentation. Experimental results show that the recall and precision values of the algorithm reach 97.5% and 93.8%, respectively, the segmentation error rate is low, and it can achieve effective segmentation of music fragments, indicating that the algorithm is effective.

Assessing Transfer Entropy in cardiovascular and respiratory time series under long-range correlations.

Heart Period (H) results from the activity of several coexisting control mechanisms, involving Systolic Arterial Pressure (S) and Respiration (R), which operate across multiple time scales encompassing not only short-term dynamics but also long-range correlations. In this work, multiscale representation of Transfer Entropy (TE) and of its decomposition in the network of these three interacting processes is obtained by extending the multivariate approach based on linear parametric VAR models to the Vector AutoRegressive Fractionally Integrated (VARFI) framework for Gaussian processes. This approach allows to dissect the different contributions to cardiac dynamics accounting for the simultaneous presence of short and long term dynamics. The proposed method is first tested on simulations of a benchmark VARFI model and then applied to experimental data consisting of H, S and R time series measured in healthy subjects monitored at rest and during mental and postural stress. The results reveal that the proposed method can highlight the dependence of the information transfer on the balance between short-term and long-range correlations in coupled dynamical systems.

Exploring volatility of carbon price in European Union due to COVID-19 pandemic.

The European Union (EU) Emissions Trading System is the most important means for the EU to achieve carbon neutrality, but it has been severely affected by the outbreak of COVID-19 in Europe, and carbon price have fluctuated sharply. Research on the driving factors of carbon price during this period will help maintain the stability of the carbon emissions trading market and promote the realization of carbon neutrality. This study selected the EU carbon allowance futures price as the research object and applied the Bai–Perron structural break test to analyze the factors that influences carbon price fluctuations using the Johansen cointegration technique and the Newey–West regression estimation. Studies have shown that the outbreak of COVID-19 and the “€750 billion green recovery plan” both had a significant impact on EU carbon price. Carbon price has also undergone significant structural changes. Under the influence of these two factors, the relationship between the level of economic development and carbon price displayed a short-term negative correlation. At the same time, oil price and interbank dismantling rates were also important factors affecting carbon price, while the impact of the clean development mechanism on carbon price was not significant. The study confirmed the effectiveness of the EU’s “green recovery plan” in stabilizing the carbon market during the COVID-19 pandemic and will provide a reference for the formulation of economic recovery policies of countries around the world.

A multi-factor integrated model for carbon price forecasting: Market interaction promoting carbon emission reduction

Reasonable carbon price can effectively promote the low-carbon transformation of economy. The future carbon price has an important guiding significance for enterprises and the country. However, the nonlinear and high noise characteristics inherent in carbon price make them challenging to predict accurately. A hybrid decomposition and integration prediction model is proposed using the Hodrick-Prescott filter, an improved grey model and an extreme learning machine to solve this problem. First, a large number of factors that influence carbon price are collected by meta-analysis. The final input is selected through a two-stage feature selection process. Second, the HP filter is used to decompose the input into long-term trends and short-term fluctuations predicted by the improved GM and ELM, respectively. Finally, the two prediction sequences are compared to obtain the final result. European Union Allowances futures price data are applied for empirical analysis. The results show that the prediction performance of this model is better than the other 10 benchmark models, the T-bill, Stoxx50, S&P clean energy index and Brent oil price in the financial and energy markets are helpful in the carbon price's prediction. T-bill affects carbon price frequently, Stoxx50 has a negative correlation with the carbon price in the influence period. Under normal circumstances, the S&P clean energy index is positively correlated with the carbon price. However, when the economic situation is depressed, resulting in a short-term negative correlation between them. In general, carbon market is significantly affected by cross spill over between different markets. The method not only improves the accuracy of carbon price forecast, but also the application of the improved GM explains the reasons for the change of carbon price, which is helpful to promote the realization of carbon neutralization by market-oriented means.

Discourses of Inverted Yield Curve and the Reaction on US Stock Markets

An outlook model to predict future economic growth is crucially important for government, financial institution, corporate, investor, as well as household. An inverted yield curve has been trusted for long time as a prediction for economic recession. But current development showed that there is change in IYC pattern that might be because of the new normal of market condition, anxious investor, or something else, hence it created the discourse among market stakeholder whether it remain a reliable prediction model or not. The research will focus on discourse among important economic stakeholders in US market and how they might impact on stock market rationally or irrationally using discourses network analysis, graph analysis, and t-Test analysis. The result confirmed that there is a different pattern on IYC, also there is a short-term correlation between IYC and stock price movement, confirmed the information theory.