scholarly journals The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach

Biology ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 125
Author(s):  
Mohammad Reza Davahli ◽  
Waldemar Karwowski ◽  
Krzysztof Fiok ◽  
Atsuo Murata ◽  
Nabin Sapkota ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Network Dynamics ◽  
Clustering Coefficient ◽  
Series Data ◽  
Spreading Dynamics ◽  
The Us ◽  
Graph Theoretical Approach ◽  
Complex Phenomena ◽  
Global And Local

Coronavirus disease 2019 (COVID-19) was first discovered in China; within several months, it spread worldwide and became a pandemic. Although the virus has spread throughout the globe, its effects have differed. The pandemic diffusion network dynamics (PDND) approach was proposed to better understand the spreading behavior of COVID-19 in the US and Japan. We used daily confirmed cases of COVID-19 from 5 January 2020 to 31 July 2021, for all states (prefectures) of the US and Japan. By applying the pandemic diffusion network dynamics (PDND) approach to COVID-19 time series data, we developed diffusion graphs for the US and Japan. In these graphs, nodes represent states and prefectures (regions), and edges represent connections between regions based on the synchrony of COVID-19 time series data. To compare the pandemic spreading dynamics in the US and Japan, we used graph theory metrics, which targeted the characterization of COVID-19 bedhavior that could not be explained through linear methods. These metrics included path length, global and local efficiency, clustering coefficient, assortativity, modularity, network density, and degree centrality. Application of the proposed approach resulted in the discovery of mostly minor differences between analyzed countries. In light of these findings, we focused on analyzing the reasons and defining research hypotheses that, upon addressing, could shed more light on the complex phenomena of COVID-19 virus spread and the proposed PDND methodology.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI IMPOR BERAS DI INDONESIA

2019 ◽  
Vol 11 (2) ◽  
pp. 183-201
Author(s):  
Yona Namira ◽  
Iskandar Andi Nuhung ◽  
Mudatsir Najamuddin
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Time Series Data ◽  
Series Data ◽  
Test Results ◽  
The Us ◽  
Rice Prices ◽  
Us Dollar ◽  
Spss Software

This study aims to 1) identify factors that affect the import of rice in Indonesia 2) analyze the influence of these factors on imports of rice in Indonesia. The data used in this research are time series data from 1994 to 2013 from the Central Statistics Agency (BPS), the Ministry of Agriculture, Ministry of Commerce, National Logistics Agency (Bulog), and Bank Indonesia. Multiple linear regression through SPSS software version 21 was employed to analyze the data. The test results together indicated the variables of productions, consumptions, stocks of rice, domestic rice prices, international rice prices and the rupiah against the US dollar affect the imports of rice in Indonesia.

scholarly journals Fractal dimensions of wildfire spreading

2014 ◽  
Vol 21 (4) ◽  
pp. 815-823 ◽  
Author(s):  
S.-L. Wang ◽  
H.-I. Lee ◽  
S.-P. Li
Keyword(s):  
Time Series ◽  
Cellular Automata ◽  
Empirical Data ◽  
Time Series Data ◽  
Numerical Study ◽  
Fractal Dimensions ◽  
Series Data ◽  
Geological Features ◽  
The Us ◽  
Good Agreement

Abstract. The time series data of 31 wildfires in 2012 in the US were analyzed. The fractal dimensions (FD) of the wildfires during spreading were studied and their geological features were identified. A growth model based on the cellular automata method is proposed here. Numerical study was performed and is shown to give good agreement with the fractal dimensions and scaling behaviors of the corresponding empirical data.

scholarly journals Pemodelan Data Time Series dengan Pendekatan Regresi Nonparametrik B-Spline

AKSIOMA ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 9-16
Author(s):  
Zulaiha Rahasia ◽  
Resmawan Resmawan ◽  
Dewi Rahmawaty Isa
Keyword(s):  
Time Series ◽  
Cross Validation ◽  
Time Series Data ◽  
Series Data ◽  
B Spline ◽  
Generalized Cross Validation ◽  
Nonparametric Approach ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
The Us

Spline is one of the nonparametric approach, to adjust data so the final model has good flexibility. The purpose of this research is to model the time series data in the form of currency exchange rates by using the nonparametric B-spline approach. In B-spline modelling, determination of the order for the model, and the number and the placement of the knot are the criteria that must be considered. The best B-spline model obtained based on the selection of the optimal knot points with minimum Generalized Cross Validation (GCV) criteria. The modelling in this research use data on the exchange rate of the rupiah toward the US dollar in the period January 2014 - December 2018. The best B-spline model obtained by the 2 point knot approach, at points 11935.10 and 12438.29, with GCV valueequals to 55683.09.Keywords: Nonparametric Regression; B-Spline; Generalized Cross Validation

scholarly journals Weighted recurrence networks for the analysis of time-series data

2019 ◽  
Vol 475 (2221) ◽  
pp. 20180256 ◽  
Author(s):  
Rinku Jacob ◽  
K. P. Harikrishnan ◽  
R. Misra ◽  
G. Ambika
Keyword(s):  
Time Series ◽  
Complex Networks ◽  
Chaotic Attractor ◽  
Time Series Data ◽  
Strength Distribution ◽  
Clustering Coefficient ◽  
Series Data ◽  
Characteristic Path Length ◽  
New Class ◽  
Analysis Of Time Series

Recurrence networks (RNs) have become very popular tools for the nonlinear analysis of time-series data. They are unweighted and undirected complex networks constructed with specific criteria from time series. In this work, we propose a method to construct a ‘weighted recurrence network’ from a time series and show that it can reveal useful information regarding the structure of a chaotic attractor which the usual unweighted RN cannot provide. Especially, a network measure, the node strength distribution, from every chaotic attractor follows a power law (with exponential cut off at the tail) with an index characteristic to the fractal structure of the attractor. This provides a new class among complex networks to which networks from all standard chaotic attractors are found to belong. Two other prominent network measures, clustering coefficient and characteristic path length, are generalized and their utility in discriminating chaotic dynamics from noise is highlighted. As an application of the proposed measure, we present an analysis of variable star light curves whose behaviour has been reported to be strange non-chaotic in a recent study. Our numerical results indicate that the weighted recurrence network and the associated measures can become potentially important tools for the analysis of short and noisy time series from the real world.

scholarly journals Approaching Stock Market Trading with Echo State Networks

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Dev Patel ◽  
Krish Patel ◽  
Charles Dela Cuesta
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Prediction Models ◽  
Time Series Data ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Modern Society ◽  
Series Data ◽  
Echo State Networks ◽  
The Us

The US stock market is an integral part of modern society. Nearly 55% of Americans  own corporate shares in the US stock market (What Percentage of Americans Own Stock?, 2019), and as of June 30th, 2020, the total value of the US stock market was over 35 trillion USD (Total Market Value of U.S. Stock Market, 2020). The stock market is also extremely volatile, and many people have gone bankrupt from poor investments. To minimize the risk and capitalize off the massive amounts of data on corporations and share prices present in the world, algorithmic trading began to rise. Trading algorithms have the potential for huge returns, and while many algorithms employ strategies like Long-Short Equity, very few attempt to use machine learning due to the unpredictable nature of the stock market. Many time series prediction models like autoregressive integrated moving average (ARIMA), and even neural networks like long short term memory (LSTMs) often fail when predicting stock market data, because unlike other time series data, the stock market is almost never univariate, or follows seasonal trends. However, where other models come short, echo state networks (ESNs) excel, due to their reservoir like computing model, which allows them to perform better on messy, non traditional time series data. Using a combination of ESNs to predict prices, and clustering we created an algorithm model that can predict trends with over 95% confidence, but had mixed results accurately predicting returns.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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