scholarly journals Score-Driven Time Series Models

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Andrew C. Harvey
Keyword(s):  
Time Series ◽  
Circular Data ◽  
Annual Review ◽  
Time Series Models ◽  
Publication Date ◽  
Time Series Modeling ◽  
Dynamic Correlation ◽  
Switching Regimes ◽  
Wide Range ◽  
Application Volume

The construction of score-driven filters for nonlinear time series models is described, and they are shown to apply over a wide range of disciplines. Their theoretical and practical advantages over other methods are highlighted. Topics covered include robust time series modeling, conditional heteroscedasticity, count data, dynamic correlation and association, censoring, circular data, and switching regimes. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Flexible Models for Complex Data with Applications

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Christophe Ley ◽  
Slađana Babić ◽  
Domien Craens
Keyword(s):  
Online Publication ◽  
Probability Distributions ◽  
Building Blocks ◽  
Circular Data ◽  
Annual Review ◽  
Publication Date ◽  
Complex Data ◽  
Heavy Tailed ◽  
Application Volume ◽  
Flexible Models

Probability distributions are the building blocks of statistical modeling and inference. It is therefore of the utmost importance to know which distribution to use in what circumstances, as wrong choices will inevitably entail a biased analysis. In this article, we focus on circumstances involving complex data and describe the most popular flexible models for these settings. We focus on the following complex data: multivariate skew and heavy-tailed data, circular data, toroidal data, and cylindrical data. We illustrate the strength of flexible models on the basis of concrete examples and discuss major applications and challenges. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 8 is March 8, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The Basis of Navigation Across Species

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Cody A. Freas ◽  
Ken Cheng
Keyword(s):  
Large Scale ◽  
Error Reduction ◽  
Annual Review ◽  
Publication Date ◽  
Additional Component ◽  
Oscillatory Systems ◽  
Neural Architecture ◽  
Wide Range ◽  
Reduction Strategies ◽  
Cycling Behavior

Animals navigate a wide range of distances, from a few millimeters to globe-spanning journeys of thousands of kilometers. Despite this array of navigational challenges, similar principles underlie these behaviors across species. Here, we focus on the navigational strategies and supporting mechanisms in four well-known systems: the large-scale migratory behaviors of sea turtles and lepidopterans as well as navigation on a smaller scale by rats and solitarily foraging ants. In lepidopterans, rats, and ants we also discuss the current understanding of the neural architecture which supports navigation. The orientation and navigational behaviors of these animals are defined in terms of behavioral error-reduction strategies reliant on multiple goal-directed servomechanisms. We conclude by proposing to incorporate an additional component into this system: the observation that servomechanisms operate on oscillatory systems of cycling behavior. These oscillators and servomechanisms comprise the basis for directed orientation and navigational behaviors. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Engineering Advances in Spray Drying for Pharmaceuticals

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
John M. Baumann ◽  
Molly S. Adam ◽  
Joel D. Wood
Keyword(s):  
Spray Drying ◽  
Oral Delivery ◽  
Process Development ◽  
Solid Dispersions ◽  
Amorphous Solid ◽  
Annual Review ◽  
Publication Date ◽  
Dry Powders ◽  
Wide Range ◽  
Pharmaceutical Industries

Spray drying is a versatile technology that has been applied widely in the chemical, food, and, most recently, pharmaceutical industries. This review focuses on engineering advances and the most significant applications of spray drying for pharmaceuticals. An in-depth view of the process and its use is provided for amorphous solid dispersions, a major, growing drug-delivery approach. Enhanced understanding of the relationship of spray-drying process parameters to final product quality attributes has made robust product development possible to address a wide range of pharmaceutical problem statements. Formulation and process optimization have leveraged the knowledge gained as the technology has matured, enabling improved process development from early feasibility screening through commercial applications. Spray drying's use for approved small-molecule oral products is highlighted, as are emerging applications specific to delivery of biologics and non-oral delivery of dry powders. Based on the changing landscape of the industry, significant future opportunities exist for pharmaceutical spray drying. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 12 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Chemical Stability of Proteins in Foods: Oxidation and the Maillard Reaction

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Mahesha M. Poojary ◽  
Marianne N. Lund
Keyword(s):  
Maillard Reaction ◽  
Protein Oxidation ◽  
Oxidation Products ◽  
Protein Carbonyls ◽  
Annual Review ◽  
Publication Date ◽  
Food Protein ◽  
Food Proteins ◽  
Wide Range ◽  
Specific Oxidation

Protein is a major nutrient present in foods along with carbohydrates and lipids. Food proteins undergo a wide range of modifications during food production, processing, and storage. In this review, we discuss two major reactions, oxidation and the Maillard reaction, involved in chemical modifications of food proteins. Protein oxidation in foods is initiated by metal-, enzyme-, or light-induced processes. Food protein oxidation results in the loss of thiol groups and the formation of protein carbonyls and specific oxidation products of cysteine, tyrosine, tryptophan, phenylalanine, and methionine residues, such as disulfides, dityrosine, kynurenine, m-tyrosine, and methionine sulfoxide. The Maillard reaction involves the reaction of nucleophilic amino acid residues with reducing sugars, which yields numerous heterogeneous compounds such as α-dicarbonyls, furans, Strecker aldehydes, advanced glycation end-products, and melanoidins. Both protein oxidation and the Maillard reaction result in the loss of essential amino acids but may positively or negatively impact food structure and flavor. Expected final online publication date for the Annual Review of Food Science and Technology, Volume 13 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Modeling and Short-Term Forecasts of Indicators for COVID-19 Outbreak in 25 Countries at the end of March

2020 ◽  
pp. 6-20
Author(s):  
Handan Ankaralı ◽  
Nadire Erarslan ◽  
Özge Pasin ◽  
Abu Kholdun Al Mahmood
Keyword(s):  
Time Series ◽  
Medical Science ◽  
Exponential Smoothing ◽  
Seasonal Effects ◽  
Time Series Models ◽  
Double Exponential ◽  
Wide Range ◽  
Simple Exponential Smoothing ◽  
Double Exponential Smoothing ◽  
Prediction And Forecasting

Objective: The coronavirus, which originated in Wuhan, causing the disease called COVID-19, spread more than 200 countries and continents end of the March. In this study, it was aimed to model the outbreak with different time series models and also predict the indicators. Materials and Methods: The data was collected from 25 countries which have different process at least 20 days. ARIMA(p,d,q), Simple Exponential Smoothing, Holt’s Two Parameter, Brown’s Double Exponential Smoothing Models were used. The prediction and forecasting values were obtained for the countries. Trends and seasonal effects were also evaluated. Results and Discussion: China has almost under control according to forecasting. The cumulative death prevalence in Italy and Spain will be the highest, followed by the Netherlands, France, England, China, Denmark, Belgium, Brazil and Sweden respectively as of the first week of April. The highest daily case prevalence was observed in Belgium, America, Canada, Poland, Ireland, Netherlands, France and Israel between 10% and 12%.The lowest rate was observed in China and South Korea. Turkey was one of the leading countries in terms of ranking these criteria. The prevalence of the new case and the recovered were higher in Spain than Italy. Conclusion: More accurate predictions for the future can be obtained using time series models with a wide range of data from different countries by modelling real time and retrospective data. Bangladesh Journal of Medical Science Vol.19(0) 2020 p.06-20

Simulation and Analysis Methods for Stochastic Compartmental Epidemic Models

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Tapiwa Ganyani ◽  
Christel Faes ◽  
Niel Hens
Keyword(s):  
Stochastic Models ◽  
Online Publication ◽  
Real Life ◽  
Likelihood Estimation ◽  
Annual Review ◽  
Publication Date ◽  
Data Set ◽  
Incidence Data ◽  
Mixed Populations ◽  
Application Volume

This article considers simulation and analysis of incidence data using stochastic compartmental models in well-mixed populations. Several simulation approaches are described and compared. Thereafter, we provide an overview of likelihood estimation for stochastic models. We apply one such method to a real-life outbreak data set and compare models assuming different kinds of stochasticity. We also give references for other publications where detailed information on this topic can be found. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 8 is March 8, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Applying Circular Statistics to the Analysis of Monitoring Data

2007 ◽  
Vol 23 (4) ◽  
pp. 227-237 ◽  
Author(s):  
Thomas Kubiak ◽  
Cornelia Jonas
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Ambulatory Monitoring ◽  
Circular Data ◽  
Circular Statistics ◽  
Series Data ◽  
Cross Sectional ◽  
Psychological Variables ◽  
Wide Range ◽  
Time Series Approach

Abstract. Patterns of psychological variables in time have been of interest to research from the beginning. This is particularly true for ambulatory monitoring research, where large (cross-sectional) time-series datasets are often the matter of investigation. Common methods for identifying cyclic variations include spectral analyses of time-series data or time-domain based strategies, which also allow for modeling cyclic components. Though the prerequisites of these sophisticated procedures, such as interval-scaled time-series variables, are seldom met, their usage is common. In contrast to the time-series approach, methods from a different field of statistics, directional or circular statistics, offer another opportunity for the detection of patterns in time, where fewer prerequisites have to be met. These approaches are commonly used in biology or geostatistics. They offer a wide range of analytical strategies to examine “circular data,” i.e., data where period of measurement is rotationally invariant (e.g., directions on the compass or daily hours ranging from 0 to 24, 24 being the same as 0). In psychology, however, circular statistics are hardly known at all. In the present paper, we intend to give a succinct introduction into the rationale of circular statistics and describe how this approach can be used for the detection of patterns in time, contrasting it with time-series analysis. We report data from a monitoring study, where mood and social interactions were assessed for 4 weeks in order to illustrate the use of circular statistics. Both the results of periodogram analyses and circular statistics-based results are reported. Advantages and possible pitfalls of the circular statistics approach are highlighted concluding that ambulatory assessment research can benefit from strategies borrowed from circular statistics.

scholarly journals Freshwater Scarcity

2021 ◽  
Vol 46 (1) ◽  
Author(s):  
Peter H. Gleick ◽  
Heather Cooley
Keyword(s):  
Fresh Water ◽  
Water Scarcity ◽  
Sustainable Management ◽  
Ecosystem Health ◽  
Annual Review ◽  
Publication Date ◽  
Wide Range ◽  
Use Efficiency ◽  
Alternative Sources ◽  
Violent Conflicts

The availability and use of fresh water are critical for human health and for economic and ecosystem stability. But the growing mismatch between human demands and natural freshwater availability is contributing to water scarcity, affecting industrial and agricultural production and a wide range of social, economic, and political problems, including poverty, deterioration of ecosystem health, and violent conflicts. Understanding and addressing different forms of water scarcity are vital for moving toward more sustainable management and use of fresh water. We provide here a review of concepts and definitions of water scarcity, metrics and indicators used to evaluate scarcity together with strategies for addressing and reducing the adverse consequences of water scarcity, including the development of alternative sources of water, improvements in water-use efficiency, and changes in systems of water management and planning. Expected final online publication date for the Annual Review of Environment and Resources, Volume 46 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Neural Network Potentials: A Concise Overview of Methods

2022 ◽  
Vol 73 (1) ◽  
Author(s):  
Emir Kocer ◽  
Tsz Wai Ko ◽  
Jörg Behler
Keyword(s):  
Machine Learning ◽  
Atomic Structure ◽  
Electrostatic Interactions ◽  
Large Scale ◽  
Machine Learning Algorithms ◽  
Annual Review ◽  
Publication Date ◽  
Great Success ◽  
Concise Overview ◽  
Wide Range

In the past two decades, machine learning potentials (MLPs) have reached a level of maturity that now enables applications to large-scale atomistic simulations of a wide range of systems in chemistry, physics, and materials science. Different machine learning algorithms have been used with great success in the construction of these MLPs. In this review, we discuss an important group of MLPs relying on artificial neural networks to establish a mapping from the atomic structure to the potential energy. In spite of this common feature, there are important conceptual differences among MLPs, which concern the dimensionality of the systems, the inclusion of long-range electrostatic interactions, global phenomena like nonlocal charge transfer, and the type of descriptor used to represent the atomic structure, which can be either predefined or learnable. A concise overview is given along with a discussion of the open challenges in the field. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 73 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Model Rantai Markov dan Model ARIMA serta Kombinasinya dalam Memprediksi Curah Hujan di Kota Makassar

2019 ◽  
Vol 1 (1) ◽  
pp. 8
Author(s):  
Ahmad Zaki ◽  
Wahidah Sanusi ◽  
Saiful Bahri
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Time Series Data ◽  
Time Sequence ◽  
Monthly Rainfall ◽  
Series Data ◽  
Time Series Models ◽  
Time Series Modeling ◽  
Discrete Variable ◽  
Future Events

Abstrak. Curah hujan merupakan suatu data deret waktu yang bersifat kontinu, namun juga dapat diformulasikan sebagai peubah diskrit yaitu dengan menggolongkan suatu hari menjadi hujan dan tidak hujan. Curah hujan yang dicatat oleh pos hujan dapat digunakan untuk memprediksi curah hujan pada waktu yang akan datang melalui pemodelan deret waktu ARIMA musiman, Rantai Markov atau dengan campuran keduanya. Proses Markov merupakan suatu sistem stokastik di mana kejadian di masa yang akan datang bergantung pada kejadian sesaat sebelumnya Deret waktu merupakan serangkaian data yang disusun menurut urutan waktu Tujuan penelitian ini adalah untuk memodelkan dan memprediksi curah hujan dengan campuran Rantai Markov dan model deret waktu. Data yang digunakan dalam penelitian ini adalah curah hujan bulanan kota Makassar tahun 2007 sampai 2017. Campuran model deret waktu lebih sesuai digunakan untuk memprediksi curah hujan bulanan dibandingkan dengan pemodelan deret waktu saja hal ini dapat dilihat dai nilai MSE.Kata Kunci: Rantai Markov, Deret Waktu, ARIMA MusimanAbstract. Rainfall is a time series data that is continuous, but can also be formulated as a discrete variable that is by classifying one day as rainy and not rainy. Rainfall recorded by rain posts can be used to predict rainfall in the future through seasonal ARIMA time series modeling, Markov Chain or with a mixture of both. The Markov process is a stochastic system in which future events depend on the events of the previous moment. The time series is a series of data arranged in time sequence. The purpose of this study is to model and predict rainfall with a mixture of Markov Chains and time series models. The data used in this study is the monthly rainfall of Makassar city in 2007 to 2017. A mixture of time series models is more suitable to be used to predict monthly rainfall compared to modeling time series. This can be seen from the MSE value.Keywords: Markov chain, Time Series, seasonal ARIMA.

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