Optimal Lag Research Articles

Optimal time lags from causal prediction model help stratify and forecast nervous system pathology

Traditional clinical approaches diagnose disorders of the nervous system using standardized observational criteria. Although aiming for homogeneity of symptoms, this method often results in highly heterogeneous disorders. A standing question thus is how to automatically stratify a given random cohort of the population, such that treatment can be better tailored to each cluster’s symptoms, and severity of any given group forecasted to provide neuroprotective therapies. In this work we introduce new methods to automatically stratify a random cohort of the population composed of healthy controls of different ages and patients with different disorders of the nervous systems. Using a simple walking task and measuring micro-fluctuations in their biorhythmic motions, we combine non-linear causal network connectivity analyses in the temporal and frequency domains with stochastic mapping. The methods define a new type of internal motor timings. These are amenable to create personalized clinical interventions tailored to self-emerging clusters signaling fundamentally different types of gait pathologies. We frame our results using the principle of reafference and operationalize them using causal prediction, thus renovating the theory of internal models for the study of neuromotor control.

The Relationship Between Foreign Direct Investment Oriented Economic Growth in Uganda: An Empirical Study Based on VAR Model

The purpose of this paper is to investigate the relationship between Foreign Direct Investment (FDI) and Economic growth as measured by Gross Domestic Product (GDP) over Uganda, from 1980-2018. Vector Autoregressive Model (VAR) and Granger Causality test were used. The results show thatlag 1 is the optimal lag hence bivariate VAR (1) model was used. GDP and FDI exhibits long-term equilibrium since the two-time series are cointegrated in long run. The causality test indicates that there exists a unilateral relationship between FDI and GDP, and FDI causes GDP growth and not vice versa. Understanding these causality links can help in future forecasting of Uganda's economic growth.

Blood Oxygen Level-Dependent Lag Patterns Differ Between Rest and Task Conditions, but Are Largely Typical in Autism.

Background/Introduction: Autism spectrum disorder (ASD) is characterized by atypical functional connectivity (FC) within and between distributed brain networks. However, FC findings have often been inconsistent, possibly due to a focus on static FC rather than brain dynamics. Lagged connectivity analyses aim at evaluating temporal latency, and presumably neural propagation, between regions. This approach may, therefore, reveal a more detailed picture of network organization in ASD than traditional FC methods. Methods: The current study evaluated whole-brain lag patterns in adolescents with ASD (n = 28) and their typically developing peers (n = 22). Functional magnetic resonance imaging data were collected during rest and during a lexico-semantic decision task. Optimal lag was calculated for each pair of regions of interest by using cross-covariance, and mean latency projections were calculated for each region. Results: Latency projections did not regionally differ between groups, with the same regions emerging among the "earliest" and "latest." Although many of the longest absolute latencies were preserved across resting-state and task conditions, lag patterns overall were affected by condition, as many regions shifted toward zero-lag during task performance. Lag structure was also strongly associated with literature-derived estimates of arterial transit time. Discussion: Results suggest that lag patterns are broadly typical in ASD but undergo changes during task performance. Moreover, lag patterns appear to reflect a combination of neural and vascular sources, which should be carefully considered when interpreting lagged FC. Impact statement Altered brain dynamics have been proposed in autism spectrum disorder (ASD). Lagged functional connectivity analysis uses cross-correlation between functional magnetic resonance imaging (fMRI) time series to determine regional latency. Few studies have examined blood oxygen level-dependent (BOLD) lag in ASD, and findings have been inconsistent. Using multi-echo fMRI data with improved artifact detection and removal, we find differences in lag structure between task and rest states, but not between adolescents with ASD and typically developing peers. Additional analyses exploring links with arterial transit time, however, highlight the impact of vascular organization on BOLD lag patterns and its potential to confound measures of neural dynamics.

Exploring the Dynamics of Global Plate Motion Based on the Granger Causality Test

The geodynamic mechanism is the research focus and core issue of plate motions and plate tectonics. Analyzing the time series of earthquakes may help us understand the relationship between two plate boundaries and further explore movement mechanisms. Therefore, this paper uses earthquake event data and the Granger causality test method to quantitatively analyze the interaction and energy transfer relationship of plate boundaries from the viewpoint of statistics. The paper aims to explore the relationship between the pull effect and the push effect of plate motion and to provide knowledge to explore seismic energy transfer relationships, and even to predict earthquakes: (1) The directions of the global plate motion field are opposite to the directions of Granger causality between plate boundaries of the Pacific, Nazca, African, Australian, Eurasian, and Philippine plates. (2) The slab-pull force (not limited to the subduction force of the ocean plates) provides a main driving force for plate motions in the Pacific plate, Nazca plate, African plate, Australian plate, Eurasian plate, and Philippine sea plate. (3) The causality relationship and optimal lag length of energy release between plate boundaries may provide another view to forecasting earthquakes.

Forecasting the COVID-19 Epidemic by Integrating Symptom Search Behavior Into Predictive Models: Infoveillance Study.

BackgroundPrevious studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions.ObjectiveThe aim of this study is to investigate the relationship between Google Trends searches of symptoms associated with COVID-19 and confirmed COVID-19 cases and deaths. We aim to develop predictive models to forecast the COVID-19 epidemic based on a combination of Google Trends searches of symptoms and conventional COVID-19 metrics.MethodsAn open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal component analysis (PCA) and time series modeling. The application facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected the data of nine countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (error, trend, seasonality; autoregressive integrated moving average; and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root mean square error (RMSE) of the first principal component (PC1). The predictive abilities of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only.ResultsThe degree of correlation and the best time lag varied as a function of the selected country and topic searched; in general, the optimal time lag was within 15 days. Overall, predictions of PC1 based on both search terms and COVID-19 traditional metrics performed better than those not including Google searches (median 1.56, IQR 0.90-2.49 versus median 1.87, IQR 1.09-2.95, respectively), but the improvement in prediction varied as a function of the selected country and time frame. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median 0.90, IQR 0.50-1.53 versus median 2.27, IQR 1.62-3.74, respectively).ConclusionsThe inclusion of digital online searches in statistical models may improve the nowcasting and forecasting of the COVID-19 epidemic and could be used as one of the surveillance systems of COVID-19 disease. We provide a free web application operating with nearly real-time data that anyone can use to make predictions of outbreaks, improve estimates of the dynamics of ongoing epidemics, and predict future or rebound waves.

Research on the Influence of Volatility of International Energy Commodity Futures Market on CPI in China

This article analyses the transmission path of the international commodity futures market’s impact on the Chinese economy. We use the MIDAS model and daily data to predict China’s CPI in real time. Empirical analysis results show that (1) the influence of high-frequency explanatory variables on low-frequency CPI is different. The optimal lag orders of domestic high-frequency variables are all around 23, which can be regarded as one month in practice, indicating that their CPI influence takes one month to show. (2) Both the univariate MIDAS model and the multivariate MIDAS combined prediction model have good performance in prediction accuracy. (3) The predicted results of the multivariate MIDAS combined prediction model for CPI in China’s normal months are relatively excellent. However, when exceptional circumstances occur, the prediction results will show a specific deviation, and the prediction accuracy will also be reduced. Finally, some feasible suggestions are put forward according to the research results.

Relative Humidity Predicts Day-to-Day Variations in COVID-19 Cases in the City of Buenos Aires

Possible links between the transmission of COVID-19 and meteorology have been investigated by comparing positive cases across geographical regions or seasons. Little is known, however, about the degree to which environmental conditions modulate the daily dynamics of COVID-19 spread at a given location. One reason for this is that individual waves of the disease typically rise and decay too sharply, making it hard to isolate the contribution of meteorological cycles. To overcome this shortage, we here present a case study of the first wave of the outbreak in the city of Buenos Aires, which had a slow evolution of the caseload extending along most of 2020. We found that humidity plays a prominent role in modulating the variation of COVID-19 positive cases through a negative-slope linear relationship, with an optimal lag of 9 days between the meteorological observation and the positive case report. This relationship is specific to winter months, when relative humidity predicts up to half of the variance in positive case count. Our results provide a tool to anticipate possible local surges in COVID-19 cases after events of low humidity. More generally, they add to accumulating evidence pointing to dry air as a facilitator of COVID-19 transmission.

Social Data: An Underutilized Metric for Determining Participation in COVID-19 Vaccinations.

Many measures have been taken since late 2019 to combat the coronavirus disease (COVID-19) pandemic. National, state, and local governments employed precautions, including mask mandates, stay-at-home orders, and social distancing policies, to alleviate the burden on healthcare workers and slow the spread of the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) virus until an efficacious vaccine was made widely available. By early spring of 2021, three effective and well-tolerated SARS-CoV-2 vaccines emerged and underwent broad distribution. Throughout the course of the COVID-19 vaccination campaign, several key logistical and psychological issues surfaced. Of these, access to vaccines and vaccination hesitancy are cited as two substantial hindrances towards vaccination. Noting the demand for the SARS-CoV-2 vaccine and its highly sensitive storage requirements, accurate dose allocation is critical for vaccinating the population quickly and successfully. Here, we propose the use of social data as a tool to predict vaccination participation by correlating Google searches with state-level daily vaccination. We identified a temporal and regionally-ubiquitous Google search syntax that broadly captures daily vaccination trends. By correlating trends in the search syntax with daily vaccination rates, we were able to quantify the correlation and identify optimal lag periods between Google searches and daily vaccination. This work highlights the importance of analyzing social data as a metric to effectively arrange vaccination roll-outs, identify voluntary vaccination participation, and identify inflection points in vaccination participation. In addition, social data assessments can help direct dose allocation, identify geographic areas that may seek, but lack, access to the vaccines, and actively prepare for fluctuations in vaccination demands.

Identification of preferential groundwater run-off belts through correlation between spring discharge and precipitation in Baiquan spring basin

Baiquan spring group is a typical sign of the existence of preferential groundwater run-off belts in Baiquan spring basin. The spring flow is mainly affected by the supply of karst water, and atmospheric precipitation is the most important factor affecting the supply of karst water. By using Pearson correlation coefficient method, the number of months with the maximum Pearson coefficient between the spring discharge and the precipitation at the monthly scale of each hydrological station in the water spring area is analyzed, which is called the best precipitation delay. According to the optimal precipitation lag time, the Pearson correlation coefficients of spring discharge and precipitation of each hydrological station in 2006-2013 are calculated on the three months scale and the daily scale respectively, and the hydrological stations closely related to spring discharge are found out. The results show that: the best precipitation lag time of each station is 6 months; the precipitation change of Wangsheren station, Suncun station and Wenzu station is consistent with the spring flow process, which has a relatively large impact on the spring flow. The 3 stations may be located in the preferential groundwater run-off belts bearing medium in Baiquan spring basin.

Simple and Instantaneous Causality in A Multivariate Auto-Regressive Models: An Application On Some Monetary Variables of the Egyptian Economy

Granger`s (1969) concept and definitions of causality, feedback, and instantaneous causality and Akaike final prediction error criterion and extended by Chan et al (1982) to fit a multivariate autoregressive model are used. The objective of the paper is to distinguish between simple, feedback and instantaneous causality. The notion of feedback between endogenous and exogenous variables in the bivariate AR model and its extension to the tri-variate AR models is presented. As an application to these Causality notions, the paper aimed to reach the optimal lag structure in forecasting some monetary variables in the Egyptian economy from 2005 to 2018. Variables selected were “current deposits of local currency”, “loan totals” and “quasi-money”. The three variables were correlated, and each variable was used as an endogenous function of itself lagged and the other two variables as exogeneous. The study also aimed to test if prediction is improved if current values and previous values are used in the prediction equation. .. The analysis showed that a one-way simple causal model exists from “loans total” to “current deposits of local currency”, and from “quasi-money” to “loans total”. Instantaneous causality and feedback occur between the three variables.

Enhancing visual motion discrimination by desynchronizing bifocal oscillatory activity

Visual motion discrimination involves reciprocal interactions in the alpha band between the primary visual cortex (V1) and mediotemporal areas (V5/MT). We investigated whether modulating alpha phase synchronization using individualized multisite transcranial alternating current stimulation (tACS) over V5 and V1 regions would improve motion discrimination. We tested 3 groups of healthy subjects with the following conditions: (1) individualized In-Phase V1alpha-V5alpha tACS (0° lag), (2) individualized Anti-Phase V1alpha-V5alpha tACS (180° lag) and (3) sham tACS. Motion discrimination and EEG activity were recorded before, during and after tACS. Performance significantly improved in the Anti-Phase group compared to the In-Phase group 10 and 30 min after stimulation. This result was explained by decreases in bottom-up alpha-V1 gamma-V5 phase-amplitude coupling. One possible explanation of these results is that Anti-Phase V1alpha-V5alpha tACS might impose an optimal phase lag between stimulation sites due to the inherent speed of wave propagation, hereby supporting optimized neuronal communication.

PENGARUH BOPO, FDR DAN NPF TERHADAP PROFITABILITAS (ROA) PADA BANK SYARIAH PERIODE 2009-2019

This study aims to determine the effect of operational costs on the operational income, financing to deposit ratio, and non-performing financing to profitability (ROA) in Islamic banks during 2009-2019. The data used in this study are secondary data sourced from Islamic banking financial statements accessed on the official website of the Financial Services Authority with monthly data from January 2009 to December 2019. This study used is a quantitative approach with VAR (Vector Auto-Regressive) analysis, consisting of stationarity test, optimal lag test, VAR model stability test, Granger causality test, impulse response function test, and variance decomposition test. Based on the results of this study, it concludes that NPF is more dominant in influencing profitability (ROA) in the short and long term. The results of the Granger causality test show that all variables have a causal relationship with each other, meaning that each variable has a causal relationship with each other and has a two-way relationship with other variables. The variables used in this study are BOPO, FDR, NPF. It is hoped that the next researcher can use more not only Islamic bank financial performance variables but also income, inflation, interest rates, and investment variables that are included as determinants of ROA levels in Islamic banking in Indonesia. Keywords: BOPO, FDR, NPF, ROA, VAR

Research on energy problem based on cointegration test

In this paper, the VAR model is used to determine the optimal lag order, and the dynamic relationship of each endogenous variable in the time series is predicted and analyzed, so as to effectively predict the interrelated time series variables. On the basis of the stationarity of the test variable series, this paper uses co-integration test and Granger causality test to conduct dynamic empirical analysis on the total energy amount and related variables in Shaanxi Province from 2000 to 2019. The research shows that there is a long-term stable equilibrium relationship between total energy and other variables.

A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions

Prediction of long-term rainfall patterns is a highly challenging task in the hydrological field due to random nature of rainfall events. The contribution of monthly rainfall is important in agriculture and hydrological tasks. This paper proposes two data-driven models, namely biogeography-based extreme learning machine (BBO-ELM) and deep neural network (DNN), to predict one, two, and three month-ahead rainfall over India (All-India and six other homogeneous regions). Three other data-driven models called ELM, genetic algorithm (GA)-based ELM, and particle swarm optimization (PSO)-based ELM are used to compare the performance of the proposed models. Firstly, partial autocorrelation function (PACF) is applied in all datasets to select the optimal number of lags for input to the models. Secondly, the wavelet-based data pre-processing technique is applied in selected optimal lags and feed to the proposed models for achieving higher prediction performance. To investigate the performance of proposed models, a non-parametric statistical test, Anderson–Darling’ Normality test, is performed in all India dataset. The wavelet-based proposed hybrid models show better prediction capability compared to optimal lag-based proposed models. This study shows the successful application of time-series data using proposed techniques (optimal lags-based BBO-ELM and wavelet-based DNN) in the hydrological field which may be used for risk mitigation from dreadful natural events.

Global Study of Human Heart Rhythm Synchronization with the Earth’s Time Varying Magnetic Field

Changes in geomagnetic conditions have been shown to affect the rhythms produced by the brain and heart and that human autonomic nervous system activity reflected in heart rate variability (HRV) over longer time periods can synchronize to changes in the amplitude of resonant frequencies produced by geomagnetic field-line and Schumann resonances. During a 15-day period, 104 participants located in California, Lithuania, Saudi Arabia, New Zealand, and England underwent continuous ambulatory HRV monitoring. The local time varying magnetic field (LMF) intensity was obtained using a time synchronized and calibrated network of magnetometers located at five monitoring sites in the same geographical locations as the participant groups. This paper focuses on the results of an experiment conducted within the larger study where all of the participants simultaneously did a heart-focused meditation called a Heart Lock-In (HLI) for a 15-min period. The participant’s level of HRV coherence and HRV synchronization to each other before, during and after the HLI and the synchronization between participants’ HRV and local time varying magnetic field power during each 24-h period were computed for each participant and group with near-optimal chaotic attractor embedding techniques. In analysis of the participants HRV coherence before, during and after the HLI, most of the groups showed significantly increased coherence during the HLI period. The pairwise heart rhythm synchronization between participants’ in each group was assessed by determining the Euclidean distance of the optimal time lag vectors of each participant to all other participants in their group. The group member’s heart rhythms were significantly more synchronized with each other during the HLI period in all the groups. The participants’ daily LMF-HRV-synchronization was calculated for each day over an 11-day period, which provided a 5-day period before, the day of and 5-days after the HLI day. The only day where all the groups HRV was positively correlated with the LMF was on the day of the HLI and the synchronization between the HRV and LMF for all the groups was significantly higher than most of the other days.

A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States

Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.

When to wake up? The optimal waking-up strategies for starvation-induced persistence.

Prolonged lag time can be induced by starvation contributing to the antibiotic tolerance of bacteria. We analyze the optimal lag time to survive and grow the iterative and stochastic application of antibiotics. A simple model shows that the optimal lag time can exhibit a discontinuous transition when the severeness of the antibiotic application, such as the probability to be exposed the antibiotic, the death rate under the exposure, and the duration of the exposure, is increased. This suggests the possibility of reducing tolerant bacteria by controlled usage of antibiotics application. When the bacterial populations are able to have two phenotypes with different lag times, the fraction of the second phenotype that has different lag time shows a continuous transition. We then present a generic framework to investigate the optimal lag time distribution for total population fitness for a given distribution of the antibiotic application duration. The obtained optimal distributions have multiple peaks for a wide range of the antibiotic application duration distributions, including the case where the latter is monotonically decreasing. The analysis supports the advantage in evolving multiple, possibly discrete phenotypes in lag time for bacterial long-term fitness.

Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: \u2013 evidence from Baidu index

BackgroundNew coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19.MethodsWe collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman’s correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed.ResultsDaily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: rs=0.705, p=9.623× 10− 6; cough: rs=0.592, p=4.485× 10− 4; fatigue: rs=0.629, p=1.494× 10− 4; sputum production: rs=0.648, p=8.206× 10− 5; shortness of breath: rs=0.656, p=6.182× 10–5). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value’s optimal time lags were the 4 days for cough, 2 days for fatigue, 3 days for sputum production, 1 day for shortness of breath, and 0 days for fever.ConclusionThe searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public’s attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.

The Cost and Consequences of Failed Osteosynthesis of Intertrochanteric Femur Fractures: A Matched Cohort Study.

We aimed to evaluate the cost and consequences of failed osteosynthesis of intertrochanteric femur fracture (ITFF) patients and compare with primary ITFF patients. We retrospectively evaluated 689 patients who underwent surgery due to ITFF via cephalomedullary nail. 31 patients (5.8%) had revision surgery because of osteosynthesis failure of ITFF. Each revision case included in the study was matched with four primary ITFF cases as control group based on age, gender, year of operation, type of fracture and American Society of Anesthesiologists (ASA) grade. Total cost for the admission that patients underwent surgery, mortality rate at first year, infection rate, length of stay at hospital, length of stay at intensive care unit, and erythrocyte transfusion amounts were recorded from hospital registry records. Tip apex distances (TAD) were noted. The mean total cost of the revision cases and primary cases was 10,027 ± 6387 and 5261 ± 1773 Turkish Liras, respectively (p < 0.001). TAD was ≥ 20mm in 32.3% (10/31) of patients in revision group while 2.4% (3/124) of the patients in control group (p < 0.001). The mean length of stay at hospital, length of stay at intensive care unit, erythrocyte transfusion amounts, infection rate and mortality rate at first year were significantly higher in revision cases compared to matched primary control cases (p < 0.05). Revision surgeries due to failed osteosynthesis of ITFFs with cephalomedullary nail have at least two times higher mean total cost than primary cases. The awareness of the cost, morbidity and mortality of the revision surgeries may reduce the modifiable risk factors of osteosynthesis failure including maintenance of TAD below 20mm, obtaining optimal lag screw position and reduction quality. Level 3, retrospective cohort study.

Finite Sample Lag Adjusted Critical Values of the ADF-GLS Test

Ng and Perron (Econometrica 69:1519–1554, 2001) demonstrated the merits to employing their Modified Akaike Information Criterion to select the optimal lag length in the Elliott, Rothenberg and Stock (Econometrica 64:813–836, 1996) unit rot test. Perron and Qu (Econ Lett 84:12–19, 2007) introduced an empirical method that resolved an associated power problem for non-local alternatives. While Cheung and Lai (Oxford Bull Econ Stat 57:411-419, 1995) contains response surface estimates to generate finite-sample, lag-adjusted critical five and ten percent values for use in applied work, these relate to the original Elliott et al. (Econometrica 64:813–836, 1996) test. This paper provides response surfaces estimates of critical values for both the Ng and Perron (Econometrica 69:1519–1554, 2001) and Perron and Qu (Econ Lett 84:12–19, 2007) approaches, demonstrating they are sometimes quite different, an important consideration when performing inference.