Time Series Analysis Approach Research Articles

Modeling epidemic dynamics using Graph Attention based Spatial Temporal networks.

The COVID-19 pandemic and influenza outbreaks have underscored the critical need for predictive models that can effectively integrate spatial and temporal dynamics to enable accurate epidemic forecasting. Traditional time-series analysis approaches have fallen short in capturing the intricate interplay between these factors. Recent advancements have witnessed the incorporation of graph neural networks and machine learning techniques to bridge this gap, enhancing predictive accuracy and providing novel insights into disease spread mechanisms. Notable endeavors include leveraging human mobility data, employing transfer learning, and integrating advanced models such as Transformers and Graph Convolutional Networks (GCNs) to improve forecasting performance across diverse geographies for both influenza and COVID-19. However, these models often face challenges related to data quality, model transferability, and potential overfitting, highlighting the necessity for more adaptable and robust approaches. This paper introduces the Graph Attention-based Spatial Temporal (GAST) model, which employs graph attention networks (GATs) to overcome these limitations by providing a nuanced understanding of epidemic dynamics through a sophisticated spatio-temporal analysis framework. Our contributions include the development and validation of the GAST model, demonstrating its superior forecasting capabilities for influenza and COVID-19 spread, with a particular focus on short-term, daily predictions. The model's application to both influenza and COVID-19 datasets showcases its versatility and potential to inform public health interventions across a range of infectious diseases.

Agricultural Production and Agricultural Employment Rate in South Africa: Time Series Analysis Approach

South Africa's agricultural sector, despite facing high unemployment, is acknowledged for creating job opportunities. However, there is concern that the sector's adoption of technology-intensive methods may not necessarily lead to increased employment. The study aims to analyze the intricate relationship between agricultural output and employment in South Africa using time series analysis. This research employed a Vector Autoregressive (VAR) model to evaluate the link between agricultural production and the rate of agricultural employment in South Africa spanning from 1990 to 2022. The findings indicate that both variables passed levels and became stationarity at the first difference when employing ADF, and a long-term equilibrium relationship between the variables was observed using Johansen cointegration test. Over the short term, there was a significant positive correlation among agricultural production and the agricultural employment rate evidenced from ECT coefficient of 0.139 value. The results of the Granger causality tests indicated unidirectional relationship that agricultural employment Granger-causes agricultural production, signifying that agricultural employment can be used to predict the growth of agricultural production. Study recommended that policies which promote injection of funds to improve production in the agriculture sector needs to be prioritized to maintain and improve employment opportunities.

EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX

This study presents an innovative financial time series analysis approach by integrating Complete Ensemble Empirical Mode Decomposition (CEEMDAN) with the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model. The primary contribution of the research lies in significantly enhancing the predictive accuracy and understanding of the dynamics governing major stock indices. CEEMDAN adaptively decomposes complex financial time series into intrinsic mode functions (IMFs), a technique that has yet to be extensively utilized in this domain. IMFs are combined with ARIMAX's predictive proficiency, which accounts for the influence of historical trends and external factors. Our study showcases an R² of 0,93, aligning with some of the high-performing models in the literature. However, the unique strength of our model lies in its lag-free predicting of the DJIA, effectively mirroring its volatility and major movements with high fidelity, making it highly practical for financial applications.

The relationship between finance and growth in Germany and Luxembourg: A timeseries analysis approach

The relationship between finance and growth in Germany and Luxembourg: A timeseries analysis approach

Analysis and future projections of the electricity demands of the Jordanian household sector using artificial neural networks

Purpose The continuous increase of energy demands is a critical worldwide matter. Jordan’s household sector accounts for 44% of overall electricity usage annually. This study aims to use artificial neural networks (ANNs) to assess and forecast electricity usage and demands in Jordan’s residential sector. Design/methodology/approach Four parameters are evaluated throughout the analysis, namely, population (P), income level (IL), electricity unit price (E$) and fuel unit price (F$). Data on electricity usage and independent factors are gathered from government and literature sources from 1985 to 2020. Several networks are analyzed and optimized for the ANN in terms of root mean square error, mean absolute percentage error and coefficient of determination (R2). Findings The predictions of this model are validated and compared with literature-reported models. The results of this investigation showed that the electricity demand of the Jordanian household sector is mainly driven by the population and the fuel price. Finally, time series analysis approach is incorporated to forecast the electricity demands in Jordan’s residential sector for the next decade. Originality/value The paper provides useful recommendations and suggestions for the decision-makers in the country for dynamic planning for future resource policies in the household sector.

Analysis of Asset Management Ratio and Solvency Management Ratio

Profit is total income after deducting company costs or what can be called net profit. Profits generated by a company can be a benchmark for a company's success. To gain large profits, a company must be able to manage assets effectively and efficiently, because this can show that the company has good financial performance. One of the ratios that investors most often consider is the total assets turnover ratio. However, often a high asset management ratio does not indicate the company's effectiveness in managing high assets, but because inventory levels are low. A company is said to be insolvent when its debt position is greater than the total assets owned by the company. To find out whether it is solvable or not, it can be calculated using the debt to equity ratio. PT FKS Food Sejahtera Tbk (IDX: AISA) is a food producing company headquartered in Jakarta, Indonesia. This company was founded in 1990, with the main products being various types of ready-made food. The purpose of this research is to analyze asset management ratio and solvency management ratio PT FKS Food Sejahtera Tbk. The type of research in this research uses qualitative descriptive research. The subject of this research is PT FKS Food Sejahtera Tbk whose financial report data is from 2015 to 2021. The object of this research is asset management ratio and solvency management at PT FKS Food Sejahtera Tbk. The analysis method in this research uses a time series analysis approach. The results of this research show that the average fixed asset turnover of PT FKS Food Sejahtera Tbk is 1.15 times. The average total asset turnover of PT FKS Food Sejahtera Tbk is 0.7 times. The average debt ratio at PT FKS Food Sejahtera Tbk from 2015 to 2017 has a value of 1.38 which becomes unsafe in 2017, 2018, and 2019. The multiple that received the highest profit was in 2019 with a multiple of 12.15.

Refined composite multiscale dynamic causality diagram: a novel time-series analysis approach and its application in fault diagnosis of rotating machinery

Entropy-based feature extraction methods have been widely used in the fault diagnosis of rotating machinery, but the entropy-based methods still have the defects of poor noise robustness, weak feature extraction, and low computational efficiency. To solve this problem, this article proposes a fault diagnosis method based on refined composite multiscale dynamic causal diagram (RCMSDCD) and local receptive field extreme learning machine (LRFELM). First, a novel feature extraction method, named dynamic causal diagram (DCD), is proposed to comprehensively quantify static and dynamic complexity. DCD is obtained by combining generalized inverse fractional order entropy with complexity–entropy causal plane. Then, combined with the coarse-graining process, DCD is extended to a multiscale analysis called RCMSDCD to complement the feature description at cross-time scales. Third, RCMSDCD features are input into LRFELM classifier for fault recognition of rotating machinery. The effectiveness of the proposed RCMSDCD-LRFELM method is verified by the Paderborn University bearing test and real wind turbine gear signals. The results show that this method has the highest classification accuracy of 100% with high computational efficiency, good stability, and strong generalization ability.

Predictive Energy Management for Docker Containers in Cloud Computing: A Time Series Analysis Approach

Predictive Energy Management for Docker Containers in Cloud Computing: A Time Series Analysis Approach

ATTRIBUTION ANALYSIS OF CLIMATE CHANGE AND HUMAN ACTIVITIES TO WATER VOLUME VARIATION IN LARGE LAKES

Abstract. Lakes are important components of the Earth's surface water bodies, and their fluctuating water levels and changing water volumes can have an important impact on ecological processes and landscape patterns within a watershed. Climate change and human activities are the main drivers of water volume changes in lakes. We applied a long time series analysis approach using classical statistics and geographic information system (GIS) technics to quantitatively attribute water volume variations in Lake Victoria from 2000 to 2019. We examined several drivers of lake water volume variations: meteorological factors including regional climate change (precipitation, evaporation) and global climate change (El Niño/Southern Oscillation events), and anthropogenic factors including socio-economic development and land use/cover change. The results show that regional climate change has a greater impact on lake water volume variations than global climate change; among them, the correlation coefficient between precipitation and lake water volume variations is the largest (0.410), with an impact lag of 1 month. For anthropogenic factors, variations in lake water volume are more pronounced in areas with rapid economic development and high population growth, with correlation coefficients greater than 0.440. Different types of land use/cover have varying impacts on Lake Victoria's water volume variations. Overall, the influence of anthropogenic factors is greater than that of meteorological factors. This study results not only provides a scientific basis for exploring the hydrological changes and their drivers in the world's large transboundary lakes, but also be great scientific significance to rational use of water resources and sustainable regional development.

On the price dynamics of non-fungible tokens: The 'Bored Apes' case.

We analyse the pattern of daily price of a collection of artistic non-fungible tokens, namely, the "Bored Ape Yacht Club" (BAYC) collectibles, over the first year of their life, from May 2021 to May 2022. Taking a time-series analysis approach, we consider the daily average price, and other variants of daily price index, derived from hedonic regression model. Aesthetic features of the collectibles do matter. At the same time, the price series emerge to be non-stationary, integrated of order 1, with their first difference exhibiting heteroscedasticity and autoregressive variance. Models of ARCH/GARCH class are appropriate to describe the dynamics. Though the price series of BAYC collectibles and their daily movements share many characteristics with the series of financial assets, they do not appear to be related to financial variables from both the crypto- and the real (i.e., not crypto) world.

Kolmogorov–Smirnov type testing for structural breaks: A new adjusted-range based self-normalization approach

A popular self-normalization (SN) approach in time series analysis uses the variance of a partial sum as a self-normalizer. This is known to be sensitive to irregularities such as persistent autocorrelation, heteroskedasticity, unit roots and outliers. We propose a novel SN approach based on the adjusted-range of a partial sum, which is robust to these aforementioned irregularities. We develop an adjusted-range based Kolmogorov–Smirnov type test for structural breaks for both univariate and multivariate time series, and consider testing parameter constancy in a time series regression setting. Our approach can rectify the well-known power decrease issue associated with existing self-normalized KS tests without having to use backward and forward summations as in Shao and Zhang (2010), and can alleviate the “better size but less power” phenomenon when the existing SN approaches (Shao, 2010; Zhang et al., 2011; Wang and Shao, 2022) are used. Moreover, our proposed tests can cater for more general alternatives. Monte Carlo simulations and empirical studies demonstrate the merits of our approach.

Emotional states predict cellular immune system activity under conditions of life as it is lived: A multivariate time-series analysis approach.

The relationship between emotional states and immune system activity is characterized by bidirectional influences; however, limited information is available regarding the temporal dynamics of these effects. The goal of this investigation was to examine how these psychoimmunological interdependencies unfold over time under conditions of "life as it is lived". For this purpose, three healthy women collected their entire urine over a period of approximately two months at 12-h intervals (8 am-8 pm, 8 pm-8 am), resulting in a total of 112 to 126 consecutive measurements per subject. In addition, among other regular psychological assessments, the subjects completed the EWL-60-S, an emotional state questionnaire, each morning and evening. To assess the extent of T-helper type 1 immune activation, the neopterin per creatinine concentration was measured in the urine samples using high-pressure liquid chromatography. The dynamic relationships between the time series of the six emotional states (performance-related activity, general inactivity, extraversion/introversion, general feeling of comfort, emotional irritation, anxiety/depressiveness) and urinary neopterin levels were estimated in vector-autoregressive models and evaluated using Granger-causality tests, impulse-response functions and forecast error variance decompositions. The findings showed that emotional states explained up to 20% of the variance of urinary neopterin per creatinine levels, whereby most of the effects occurred within a period of approximately three days. Across all subjects, increases in anxiety/depressiveness and extraversion led to increases in neopterin levels, while a general feeling of comfort led to decreases in neopterin. These results emphasize the importance of the interdependencies between emotional states and immune system activity and showcase the potential that intensive longitudinal study designs offer for psychoneuroimmunology.

Mechanistic interaction between climate variables rainfall and temperature on surface water quality and water treatment costs at the Barekese Headworks, Ghana: A time series analysis and water quality index modelling approach

Extreme rainfall and temperatures are climate variables that threaten global water supplies and surface water quality (SWQ). To understand how rainfall and temperature interact with surface water quality and water treatment costs, this study, unlike previous ones, uses time series analysis (TSA) and water quality index (WQI) modelling to fill significant research gaps. The study uses data from the Barekese Water Treatment Headworks, Ghana. Water quality data from 2000 to 2019 for the Barekese Headworks were obtained from the Ghana Water Company Limited. Rainfall data for the catchment was obtained from the Ghana Meteorological Agency. The Mann-Kendall statistical test for trend and the Canadian Council of Ministers of the Environment (CCME) water quality index was applied to data sets. The Mann-Kendall trend test showed no significant change in annual temperatures. An increasing trend for annual rainfall was observed, but this was not statistically significant (Z = 0.21). Sen's slope estimator (Q) showed that rainfall increases at 3.03 mm annually. pH correlated negatively with rainfall (r = - 0.15). Correlations were observed between rainfall and temperature and dissolved oxygen (DO), turbidity, Total Dissolved Solids (TDS), Nitrate (NO3−), Phosphate (PO43−), and Manganese (Mn). Rainfall was observed to increase the cost of liming, coagulation, and disinfection. A 20.26% deterioration in SWQ was observed from 2009 to 2019. The SWQ over the period under study and according to the CCME water quality index was 80% marginal, 10% fair and 10% poor. The findings reveal that the concurrent use of TSA and WQI modelling can help elucidate how rainfall and temperature interact with SWQ and water treatment costs. It further contributes knowledge to attaining the Africa Union Agenda 2063 on climate resilience and the Sustainable Development Goals (SDG) 6 and 6.3 on universal water access and quality improvement.

Use of Ilizarov technique for bilateral knees flexion contracture in Juvenile-onset ankylosing spondylitis: A case report

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Usefulness of analyzing endoscopic features in identifying the colorectal serrated sessile lesions with and without dysplasia.

Sessile serrated lesions (SSLs) are often missed on colonoscopy, and studies have shown this to be an essential cause of interstitial colorectal cancer. The SSLs with dysplasia (SSL-D+), in particular, have a faster rate of carcinogenesis than conventional tubular adenomas. Therefore, there is a clinical need for some endoscopic features with independent diagnostic value for SSL-D+s to assist endoscopists in making immediate diagnoses, thus improving the quality of endoscopic examination and treatment. To compare the characteristics of SSLs, including those with and without dysplasia (SSL-D+ and SSL-D-), based on white light and image-enhanced endoscopy, to achieve an immediate differential diagnosis for endoscopists. From January 2017 to February 2023, cases of colorectal SSLs confirmed by colonoscopy and histopathology at the Gastrointestinal Endoscopy Center of Beijing Tsinghua Changgung Hospital were collected. The general, endoscopic, and histopathological data were reviewed and analyzed to determine the diagnostic utility. Univariate analysis was used to find potential diagnostic factors, and then multivariate regression analysis was performed to derive endoscopic features with independent diagnostic values for the SSL-D+. A total of 228 patients with 253 lesions were collected as a result. There were 225 cases of colorectal SSL-D-s and 28 cases of SSL-D+s. Compared to the colorectal SSL-D-, the SSL-D+ was more common in the right colon (P = 0.027) with complex patterns of depression, nodule, and elevation based on cloud-like surfaces (P = 0.003), reddish (P < 0.001), microvascular varicose (P < 0.001), and mixed type (Pit II, II-O, IIIL, IV) of crypt opening based on Pit II-O (P < 0.001). Multifactorial logistic regression analysis indicated that lesions had a reddish color [odds ratio (OR) = 18.705, 95% confidence interval (CI): 3.684-94.974], microvascular varicose (OR = 6.768, 95%CI: 1.717-26.677), and mixed pattern of crypt opening (OR = 20.704, 95%CI: 2.955-145.086) as the independent predictors for SSL-D+s. The endoscopic feature that has independent diagnostic value for SSL-D+ is a reddish color, microvascular varicose, and mixed pattern of crypt openings.

Acute cholangitis: Does malignant biliary obstruction vs choledocholithiasis etiology change the clinical presentation and outcomes?

Whether clinical outcomes of acute cholangitis (AC) vary by etiology is unclear. To compare outcomes in AC caused by malignant biliary obstruction (MBO) and common bile duct stones (CBDS). This retrospective study included 516 patients undergoing endoscopic retrograde cholangiopancreatography (ERCP) due to AC caused by MBO (MBO group, n = 56) and CBDS (CBDS group, n = 460). Clinical and laboratory parameters were compared between the groups. Propensity score matching (PSM) created 55 matched pairs. Confounders used in the PSM analysis were age, sex, time to ERCP, and technical success of ERCP. The primary outcome comparison was 30-d mortality. The secondary outcome comparisons were intensive care unit (ICU) admission rate, length of hospital stay (LOHS), and 30-d readmission rate. Compared with the CBDS group, the MBO group had significantly lower body temperature, percentage of abnormal white blood cell counts, and serum levels of aspartate aminotransferase, alanine aminotransferase, and creatinine. Body temperature, percent abnormal white blood cell count, and serum aspartate aminotransferase levels remained significantly lower in the MBO group in the PSM analysis. Platelet count, prothrombin time/international normalized ratio, and serum levels of alkaline phosphatase and total bilirubin were significantly higher in the MBO group. The MBO group had a significantly higher percentage of severe AC (33.9% vs 22.0%, P = 0.045) and received ERCP later (median, 92.5 h vs 47.4 h, P < 0.001). However, the two differences were not found in the PSM analysis. The 30-d mortality (5.4% vs 0.7%, P = 0.019), ICU admission rates (12.5% vs 4.8%, P = 0.028), 30-d readmission rates (23.2% vs 8.0%, P < 0.001), and LOHS (median, 16.5 d vs 7.0 d, P < 0.001) were significantly higher or longer in the MBO group. However, only LOHS remained significant in the PSM analysis. Multivariate analysis revealed that time to ERCP and multiple organ dysfunction were independent factors associated with 30-d mortality. MBO patients underwent ERCP later and thus had a worse prognosis than CBDS patients. Therefore, clinicians should remain vigilant in MBO patients with clinically suspected AC, and perform ERCP for biliary drainage as soon as possible.

Unusual presentation of penile giant condyloma acuminatum with spontaneous prepuce perforation: A case report

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Roles of biochemistry data, lifestyle, and inflammation in identifying abnormal renal function in old Chinese

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Meta-analysis of the efficacy and safety of daratumumab in the treatment of multiple myeloma.

The treatment of multiple myeloma has significantly progressed over the past half-century. The purpose of this study was to perform a systematic review and meta-analysis in order to explore the efficacy and safety of daratumumab in treating multiple myeloma. To explore the efficacy and safety of daratumumab in treating multiple myeloma. A systematic literature search was performed using Chinese and English databases, including the China National Knowledge Infrastructure, Wanfang, China Biology Medicine, VIP, the Cochrane Library, Embase, and PubMed. The search encompassed studies in treating multiple myeloma with daratumumab, spanning from the inception of the database to June 2023. Revman 5.1 software was used for analysis. Our analysis included eight English articles and one Chinese article of high quality. The meta-analysis results indicated that compared to other therapies, daratumumab could improve the overall response rate (ORR) [odds ratio (OR) = 2.67, 95% confidence interval (CI) = 2.01, 3.53, Z = 6.85, P < 0.00001], complete remission (CR) (OR = 2.87, 95%CI = 2.16, 3.83, Z = 7.23, P < 0.00001) and progression-free survival (PFS) time (hazard ratio = 0.48, 95%CI = 0.38,0.60, Z = 6.54, P < 0.00001) in patients with multiple myeloma. These differences were statistically significant. Additionally, these results suggested that daratumumab increases the risk of neutropenia and thrombocytopenia with minimal effect on the incidences of anemia and upper respiratory tract infections. Daratumumab can improve ORR, CR rate, and PFS in patients with multiple myeloma. It also increases the risk of neutropenia and thrombocytopenia, necessitating careful monitoring during its clinical application.