Microbiome dynamics analysis using a novel multivariate vector autoregression model with weighted fusion regularization

Agriculture sector throughout the world including Bangladesh is extremely vulnerable to the negative consequences of climate change as evident in a good number of studies. Accurate climate forecasting may prove a valuable resource in mitigating these consequences in agriculture. The study aims to identify the best performing forecasting method by comparing the forecasting abilities of univariate seasonal autoregressive integrated moving average (SARIMA) and multivariate vector autoregression (VAR) models in forecasting monthly maximum and minimum temperatures, humidity, and cloud coverage in Bangladesh. Though the univariate time series investigate the influence of the past values of a single time series on the future values of that specific series, the VAR approach forecast multivariate time series simultaneously incorporating the interrelationship among the groups of variables. Monthly forecasts of climatic parameters for in-sample over the period 1972-2008 and out-of-sample from 2009-2013 were generated via a univariate SARIMA and a VAR approach. Different forecast accuracy measures reveal that VAR model give better forecast than univariate SARIMA model. The forecast results using VAR(9) model from January 2014 to December 2021 show that maximum and minimum temperature, as well as humidity are increasing while the cloud coverage is decreasing, that is, consistent with global warming. Moreover, the impulse response function results exhibit the fluctuated and significant dynamic relationships in future among the foresaid climatic variables. Thus, findings of the study can potentially allow Bangladeshi farmers and other actors in the agriculture sector to make proper planning to abate unwanted impacts or reap the expected benefits of favourable climate.J. Bangladesh Agril. Univ. 16(1): 131-143, April 2018

Using a multivariate vector autoregression (VAR) model, this paper investigates if sectoral shifts, inflation uncertainty, or demand shocks are the primary cause of unemployment fluctuations in the postwar US economy. A sectoral shifts variable (cross-section volatility), an ARCH measure of inflation uncertainty, and three demand shocks variables (monetary base growth rate, interest rates and inflation rates) are incorporated in a VAR model. Our major findings are: cross-section volatility Granger causes unemployment; the sectoral shifts variable and inflation uncertainty explain a small amount, while demand shocks variables explain a substantial amount of the variation in unemployment.

The ionospheric total electron content (TEC) severely impacts the positional accuracy of a single frequency Global Positioning System (GPS) receiver at the equatorial latitudes. The ionosphere causes a frequency-dependent group delay in the GPS-ranging signals, which reduces the receiver’s accuracy. Further, the variations in TEC due to various space weather phenomena make the ionosphere’s behaviour nonhomogeneous and complex. Hence, developing an accurate forecast model that can track the dynamic behaviour of the ionosphere remains a challenge. However, advances in emerging data-driven algorithms have been found helpful in tracking non-stationary behavior in TEC. These models help forecast the delays in advance. The multivariate Vector Autoregression model (VAR) predicts the Ionospheric TEC in the proposed model. The prediction model uses input data compiled in real-time from the lag values of incoming TEC data and features extracted from TEC. The TEC is predicted in real-time and tested for different prediction intervals. The metrics – Mean Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are used for testing and validating the accuracy of the model statistically. Testing the predicted output accuracy is also done with the dynamic time warping (DTW) algorithm by comparing it with the actual value obtained from the dual-frequency receiver. The model is tested for storm days of the year 2015 for Bangalore and Hyderabad stations and found to be reliable and accurate. A prediction interval of twenty-minute shows the highest accuracy with an error within 10 TECU for all the storm days.

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying lengths (T = 25, 50, 75, 100, 125) using Statistical Analysis System (SAS) version 9.2. Autoregressive components for the 5 series vectors included coefficients of .80, .70, .65, .50 and .40. Error variance components included values of .20, .20, .10, .15, and .15, with cross-lagged coefficients of .10, .10, .15, .10, and .10. A Monte Carlo study revealed that in comparison to frequentist methods, the Bayesian approach provided increased sensitivity for hypothesis testing and detecting Type I error.

A time series analysis of disaggregate U.S. unemployment

Commodity prices have crucial implications for developing countries. The question whether the financialization of commodity derivative markets has contributed to high and volatile commodity prices has been controversially debated. Building on limitations in the empirical literature, we estimate a multivariate Vector Autoregressive (VAR) model to assess the effect of different groups of financial investors (index investors and money managers) as well as fundamental and macroeconomic variables on the prices of coffee, cotton, wheat and oil. We find that, in contrast to index investors, money managers’ net long positions have a large statistically significant effect on commodity prices. This calls for policy interventions as commodity derivative markets may cease to perform their fundamental developmental roles.

Networks are ubiquitous throughout biology, spanning the entire range from molecules to food webs and global environmental systems. Yet, despite substantial efforts by the scientific community, the inference of these networks from data still presents a problem that is unsolved in general. One frequent strategy of addressing the structure of networks is the assumption that the interactions among molecular or organismal populations are static and correlative. While often successful, these static methods are no panacea. They usually ignore the asymmetry of relationships between two species and inferences become more challenging if the network nodes represent dynamically changing quantities. Overcoming these challenges, two very different network inference approaches have been proposed in the literature: Lotka-Volterra (LV) models and Multivariate Autoregressive (MAR) models. These models are computational frameworks with different mathematical structures which, nevertheless, have both been proposed for the same purpose of inferring the interactions within coexisting population networks from observed time-series data. Here, we assess these dynamic network inference methods for the first time in a side-by-side comparison, using both synthetically generated and ecological datasets. Multivariate Autoregressive and Lotka-Volterra models are mathematically equivalent at the steady state, but the results of our comparison suggest that Lotka-Volterra models are generally superior in capturing the dynamics of networks with non-linear dynamics, whereas Multivariate Autoregressive models are better suited for analyses of networks of populations with process noise and close-to linear behavior. To the best of our knowledge, this is the first study comparing LV and MAR approaches. Both frameworks are valuable tools that address slightly different aspects of dynamic networks.

Causal relations and dynamic interactions among equity returns in ten countries for the period 1983–1994 are analysed. An innovation accounting approach based on a multivariate vector autoregressive (VAR) model is used to estimate the proportion of each market return's forecast error attributable to innovations in foreign market returns. Three major results appear. The variance decompositions indicate a strong degree of economic interaction among stock markets. The US stock market has a considerable influence on stock market performance in almost every country, while there is no substantial inter-continental influence from the European stock markets on the world's two largest equity markets in New York and Tokyo. Finally, the pattern of the impulse-response functions illustrates a rapid international transmission of stock market events, supporting the hypothesis of international stock market efficiency.

This paper presents evidence concerning shared stochastic trends in North American natural gas (spot) markets, using monthly data for the period that natural gas has been traded on organized exchanges (from June, 1990 to January, 1996). In doing so, it uses the Engle and Granger (1987) approach for estimating bivariate cointegrating relationships as well as Johansen's (1988) maximum likelihood approach for estimating cointegrating relationships in multivariate vector autoregressive models. The results indicate that the east-west split does not exist.

This paper presents evidence concerning shared stochastic trends in North American natural gas (spot) markets, using monthly data for the period that natural gas has been traded on organized exchanges (from June, 1990 to January, 1996). In doing so, it uses the Engle and Granger (1987) approach for estimating bivariate cointegrating relationships as well as Johansen's (1988) maximum likelihood approach for estimating cointegrating relationships in multivariate vector autoregressive models. The results indicate that the east-west split does not exist.

The Baillie and Bollerslev (1989) study is partly (replicated) and evidence concerning the number of common stochastic trends in a system of seven East European black-market exchange rates is presented, using Koedijk and Kool's (1992) monthly data from January 1955 to May 1990. Johansen's (1988) maximum likelihood approach for estimating long-run relations in multivariate vector autoregressive models is used. The results indicate the presence of three common trends driving these exchange rates.

The Partial Directed Coherence (PDC) and its generalized formulation (gPDC) are popular tools for investigating, in the frequency domain, the concept of Granger causality among multivariate (MV) time series. PDC and gPDC are formalized in terms of the coefficients of an MV autoregressive (MVAR) model which describes only the lagged effects among the time series and forsakes instantaneous effects. However, instantaneous effects are known to affect linear parametric modeling, and are likely to occur in experimental time series. In this study, we investigate the impact on the assessment of frequency domain causality of excluding instantaneous effects from the model underlying PDC evaluation. Moreover, we propose the utilization of an extended MVAR model including both instantaneous and lagged effects. This model is used to assess PDC either in accordance with the definition of Granger causality when considering only lagged effects (iPDC), or with an extended form of causality, when we consider both instantaneous and lagged effects (ePDC). The approach is first evaluated on three theoretical examples of MVAR processes, which show that the presence of instantaneous correlations may produce misleading profiles of PDC and gPDC, while ePDC and iPDC derived from the extended model provide here a correct interpretation of extended and lagged causality. It is then applied to representative examples of cardiorespiratory and EEG MV time series. They suggest that ePDC and iPDC are better interpretable than PDC and gPDC in terms of the known cardiovascular and neural physiologies.

This project assessed the use of multivariate auto-regressive (MAR) models to create forecasts of continuous vital signs in hospitalized patients. A total of 20 hours continuous (1/60Hz) heart rate and respiration rate from eight postoperative patients, where used to fit a centered MAR model for forecasting in windows of 15 minutes. The model was fitted using Markov Chain Monte Carlo sampling, and the model was evaluated on data from five additional patients. The results demonstrate an average RMSE in the forecast window of 11.4 (SD: 7.30) beats per minute for heart rate and 3.3 (SD:1.3) breaths per minute for respiration rate. These results indicate potential for forecasting vital signs in a clinical setting.

This study developed a driving mental fatigue estimation system based on electroencephalogram (EEG) when he drives a car in a virtual reality (VR)-based dynamic simulator. To classify driver's mental fatigue status, the features of multichannel electroencephalographic (EEG) signals of frontal, central and occipital are extracted by multivariate autoregressive (MVAR) model. Then kernel principal component analysis (KPCA) and support vector machines (SVM) are combined to identify three-levels driving mental fatigue. The results show that KPCA is an good feature extractor which can effectively reduce the dimensionality of the feature vectors. The KPCA-SVM shows good performance with higher classification accuracy (81.64%) across 10 subjects. This method could be an potential approach of classification of driving mental fatigue.

Scenario generation and probabilistic forecasting analysis of spatio-temporal wind speed series with multivariate autoregressive volatility models