Toward optimal model averaging in regression models with time series errors

This paper indicates special aspects of using vector auto-regression models to forecast rates of basic macroeconomic indicators in short term. In particular, traditional vector auto-regression model, Bayesian vector auto-regression model and factor augmented vector auto-regression model are shown. For parameter estimation of these models the author uses time series of Kazakhstani macroeconomic indicators between 1996 and 2015 quarterly. In virtue of mean-root-square error prediction the conclusion of optimal model is going to be chosen.

Download Full-text

Correlation between Acinetobacter baumannii Resistance and Hospital Use of Meropenem, Cefepime, and Ciprofloxacin: Time Series Analysis and Dynamic Regression Models

Pathogens ◽

10.3390/pathogens10040480 ◽

2021 ◽

Vol 10 (4) ◽

pp. 480

Author(s):

Rania Kousovista ◽

Christos Athanasiou ◽

Konstantinos Liaskonis ◽

Olga Ivopoulou ◽

George Ismailos ◽

...

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Acinetobacter Baumannii ◽

Regression Models ◽

Tertiary Care ◽

Antimicrobial Use ◽

Care Hospital ◽

Arima Models ◽

Series Analysis ◽

Resistance Rates

Acinetobacter baumannii is one of the most difficult-to-treat pathogens worldwide, due to developed resistance. The aim of this study was to evaluate the use of widely prescribed antimicrobials and the respective resistance rates of A. baumannii, and to explore the relationship between antimicrobial use and the emergence of A. baumannii resistance in a tertiary care hospital. Monthly data on A. baumannii susceptibility rates and antimicrobial use, between January 2014 and December 2017, were analyzed using time series analysis (Autoregressive Integrated Moving Average (ARIMA) models) and dynamic regression models. Temporal correlations between meropenem, cefepime, and ciprofloxacin use and the corresponding rates of A. baumannii resistance were documented. The results of ARIMA models showed statistically significant correlation between meropenem use and the detection rate of meropenem-resistant A. baumannii with a lag of two months (p = 0.024). A positive association, with one month lag, was identified between cefepime use and cefepime-resistant A. baumannii (p = 0.028), as well as between ciprofloxacin use and its resistance (p < 0.001). The dynamic regression models offered explanation of variance for the resistance rates (R2 > 0.60). The magnitude of the effect on resistance for each antimicrobial agent differed significantly.

Download Full-text

Using spatio-temporal land use regression models to address spatial variation in air pollution concentrations in time series studies

Air Quality Atmosphere & Health ◽

10.1007/s11869-017-0500-1 ◽

2017 ◽

Vol 10 (9) ◽

pp. 1139-1149 ◽

Cited By ~ 8

Author(s):

Konstantina Dimakopoulou ◽

Alexandros Gryparis ◽

Klea Katsouyanni

Keyword(s):

Land Use ◽

Air Pollution ◽

Time Series ◽

Spatial Variation ◽

Regression Models ◽

Land Use Regression ◽

Time Series Studies ◽

Spatio Temporal ◽

Pollution Concentrations

Download Full-text

Uncertainty quantification of the effects of biotic interactions on community dynamics from nonlinear time-series data

Journal of The Royal Society Interface ◽

10.1098/rsif.2018.0695 ◽

2018 ◽

Vol 15 (147) ◽

pp. 20180695 ◽

Cited By ~ 7

Author(s):

Simone Cenci ◽

Serguei Saavedra

Keyword(s):

Time Series ◽

Community Dynamics ◽

Time Series Data ◽

Multivariate Time Series ◽

Model Averaging ◽

Biotic Interactions ◽

Series Data ◽

Time Intervals ◽

Novel Approach ◽

Data Generating Process

Biotic interactions are expected to play a major role in shaping the dynamics of ecological systems. Yet, quantifying the effects of biotic interactions has been challenging due to a lack of appropriate methods to extract accurate measurements of interaction parameters from experimental data. One of the main limitations of existing methods is that the parameters inferred from noisy, sparsely sampled, nonlinear data are seldom uniquely identifiable. That is, many different parameters can be compatible with the same dataset and can generalize to independent data equally well. Hence, it is difficult to justify conclusive assertions about the effect of biotic interactions without information about their associated uncertainty. Here, we develop an ensemble method based on model averaging to quantify the uncertainty associated with the effect of biotic interactions on community dynamics from non-equilibrium ecological time-series data. Our method is able to detect the most informative time intervals for each biotic interaction within a multivariate time series and can be easily adapted to different regression schemes. Overall, this novel approach can be used to associate a time-dependent uncertainty with the effect of biotic interactions. Moreover, because we quantify uncertainty with minimal assumptions about the data-generating process, our approach can be applied to any data for which interactions among variables strongly affect the overall dynamics of the system.

Download Full-text

When and when not to use optimal model averaging

Statistical Papers ◽

10.1007/s00362-018-1048-3 ◽

2018 ◽

Vol 61 (5) ◽

pp. 2221-2240

Author(s):

Michael Schomaker ◽

Christian Heumann

Keyword(s):

Model Averaging ◽

Optimal Model

Download Full-text

Changes of China’s agri-food exports to Germany caused by its accession to WTO and the 2008 financial crisis

China Agricultural Economic Review ◽

10.1108/caer-11-2013-0152 ◽

2015 ◽

Vol 7 (2) ◽

pp. 262-279 ◽

Cited By ~ 3

Author(s):

Zhichao Guo ◽

Yuanhua Feng ◽

Thomas Gries

Keyword(s):

Time Series ◽

Financial Crisis ◽

Regression Models ◽

Structural Breaks ◽

Global Financial Crisis ◽

Middle Part ◽

Change Points ◽

Content Type ◽

The World ◽

Before And After

Purpose – The purpose of this paper is to investigate changes of China’s agri-food exports to Germany caused by China’s accession to WTO and the global financial crisis in a quantitative way. The paper aims to detect structural breaks and compare differences before and after the change points. Design/methodology/approach – The structural breaks detection procedures in this paper can be applied to find out two different types of change points, i.e. in the middle and at the end of one time series. Then time series and regression models are used to compare differences of trade relationship before and after the detected change points. The methods can be employed in any economic series and work well in practice. Findings – The results indicate that structural breaks in 2002 and 2009 are caused by China’s accession to WTO and the financial crisis. Time series and regression models show that the development of China’s exports to Germany in agri-food products has different features in different sub-periods. Before 1999, there is no significant relationship between China’s exports to Germany and Germany’s imports from the world. Between 2002 and 2008 the former depends on the latter very strongly, and China’s exports to Germany developed quickly and stably. It decreased, however suddenly in 2009, caused by the great reduction of Germany’s imports from the world in that year. But China’s market share in Germany still had a small gain. Analysis of two categories in agri-food trade also leads to similar conclusions. Comparing the two events we see rather different patterns even if they both indicate structural breaks in the development of China’s agri-food exports to Germany. Originality/value – This paper partly originally proposes two statistical algorithms for detecting different kinds of structural breaks in the middle part and at the end of a short-time series, respectively.

Download Full-text

Real-Time Prediction of the COVID-19 Epidemic in Thailand using Simple Model-Free Method and Time Series Regression Model

Walailak Journal of Science and Technology (WJST) ◽

10.48048/wjst.2021.10028 ◽

2021 ◽

Vol 18 (14) ◽

Author(s):

Rati WONGSATHAN

Keyword(s):

Genetic Algorithm ◽

Time Series ◽

Regression Model ◽

Regression Models ◽

Gaussian Function ◽

Logistic Function ◽

Hyperbolic Tangent ◽

Time Series Regression ◽

Model Free ◽

Tangent Function

The novel coronavirus 2019 (COVID-19) pandemic was declared a global health crisis. The real-time accurate and predictive model of the number of infected cases could help inform the government of providing medical assistance and public health decision-making. This work is to model the ongoing COVID-19 spread in Thailand during the 1st and 2nd phases of the pandemic using the simple but powerful method based on the model-free and time series regression models. By employing the curve fitting, the model-free method using the logistic function, hyperbolic tangent function, and Gaussian function was applied to predict the number of newly infected patients and accumulate the total number of cases, including peak and viral cessation (ending) date. Alternatively, with a significant time-lag of historical data input, the regression model predicts those parameters from 1-day-ahead to 1-month-ahead. To obtain optimal prediction models, the parameters of the model-free method are fine-tuned through the genetic algorithm, whereas the generalized least squares update the parameters of the regression model. Assuming the future trend continues to follow the past pattern, the expected total number of patients is approximately 2,689 - 3,000 cases. The estimated viral cessation dates are May 2, 2020 (using Gaussian function), May 4, 2020 (using a hyperbolic function), and June 5, 2020 (using a logistic function), whereas the peak time occurred on April 5, 2020. Moreover, the model-free method performs well for long-term prediction, whereas the regression model is suitable for short-term prediction. Furthermore, the performances of the regression models yield a highly accurate forecast with lower RMSE and higher R2 up to 1-week-ahead. HIGHLIGHTS COVID-19 model for Thailand during the first and second phases of the epidemic The model-free method using the logistic function, hyperbolic tangent function, and Gaussian function applied to predict the basic measures of the outbreak Regression model predicts those measures from one-day-ahead to one-month-ahead The parameters of the model-free method are fine-tuned through the genetic algorithm GRAPHICAL ABSTRACT

Download Full-text

Flexible Bayesian Nonlinear Model Configuration

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.13047 ◽

2021 ◽

Vol 72 ◽

pp. 901-942

Author(s):

Aliaksandr Hubin ◽

Geir Storvik ◽

Florian Frommlet

Keyword(s):

Regression Models ◽

Nonlinear Models ◽

Model Averaging ◽

Predictive Performance ◽

Machine Learning Algorithms ◽

Parametric Models ◽

Monte Carlo Algorithm ◽

Wide Range ◽

Nonlinear Features ◽

Interpretable Models

Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are often not sufficient to describe complex relationships between input variables and a response. Such relationships can be better described through flexible approaches such as neural networks, but this results in less interpretable models and potential overfitting. Alternatively, specific parametric nonlinear functions can be used, but the specification of such functions is in general complicated. In this paper, we introduce a flexible approach for the construction and selection of highly flexible nonlinear parametric regression models. Nonlinear features are generated hierarchically, similarly to deep learning, but have additional flexibility on the possible types of features to be considered. This flexibility, combined with variable selection, allows us to find a small set of important features and thereby more interpretable models. Within the space of possible functions, a Bayesian approach, introducing priors for functions based on their complexity, is considered. A genetically modi ed mode jumping Markov chain Monte Carlo algorithm is adopted to perform Bayesian inference and estimate posterior probabilities for model averaging. In various applications, we illustrate how our approach is used to obtain meaningful nonlinear models. Additionally, we compare its predictive performance with several machine learning algorithms.

Download Full-text