Качество планирования городских транспортных сетей в зеркале классических моделей теории транспортного потока

2015 ◽  
pp. 56-67
Author(s):  
Михаил Яковлевич Блинкин
Keyword(s):  
Time Series Data ◽  
Fluid Model ◽  
Nobel Laureate ◽  
Series Data ◽  
Load Factor ◽  
Planning Decisions ◽  
Road Design ◽  
The World ◽  
Two Fluid Model ◽  
Gps Tracks

This article investigates the application of the classical models of the traffi c fl ow theory to the analysis of the modern transport and urban planning problems in the world. As a typical case study author refers to the “Two-Fluid Model of Urban Traffi c”, proposed by the American physicist Robert Herman and Nobel laureate Ilya Prigogine in the 1970s. The choice of this case was based not only on the model’s “noble scientifi c origin”, but on its modern appeal due to emergence of large arrays of the full- scale data (eg, GPS-tracks), that was unavailable before. “Herman–Prigogine” model allows for HP-indicator (η) calculation, which characterizes the elasticity of speeds to street-road network load factor increase. It is based on a comparison of synchronous time series data of running time and travel time. The indicator determines the quality of planning decisions on street and road design. The article presents the results of the HP-indicator calculations made for a number of cities around the world in 1980–2000,

scholarly journals FAKTOR – FAKTOR YANG MEMPENGARUHI KONSUMSI GAS DI INDONESIA

2021 ◽  
Vol 3 (2) ◽  
pp. 69
Author(s):  
Rohim Rohim ◽  
Mike Triani
Keyword(s):  
Time Series ◽  
World Bank ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Industrial Growth ◽  
A Value ◽  
The World Bank ◽  
The World ◽  
Gas Consumption

The purpose of this research is to determine (1) the effect of income on gas consumption in Indonesia (2) the effect of population on gas consumption in Indonesia (3) the effect of industrial growth on gas consumption in Indonesia. This type of research is descriptive and associative. The data used in this research is secondary data from Indonesia in the form of time series data from 1970 to 2019 and this data was obtained from official institutions of the World Bank and BP Statistic World. The data were processed using multiple linear regression. The results showed that the income had a negative and significant effect on gas consumption with a probability value of 0.0005 <0.05, the population had a positive and significant effect on gas consumption with a value of prob t-count of 0.0010 <0.05 and industrial growth had a positive and significant effect on gas consumption.  The significant to gas consumption in Indonesia with a value of prob t-count value of 0.5219 <0.05 and suggestions for further researchers to be able to analyze other factors that affecting gas consumption in Indonesia.  Because from the gas sectors, there are still many factors that affected gas consumption until the research results will be better

A Deep Learning Based Methods for Forecasting Gold Price with Respect to Pandemics

2020 ◽  
Author(s):  
Mahtab Mohtasham Khani ◽  
Sahand Vahidnia ◽  
Alireza Abbasi
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Specific Model ◽  
Gold Price ◽  
Series Data ◽  
Trade Relations ◽  
Market Values ◽  
The World ◽  
Proper Estimation ◽  
Validation Set

Abstract The spread of COVID-19 in the world had a devastating impact on the world economy, trade relations, and globalization. As the pandemic advances and new potential pandemics are on the horizon, a precise analysis of recent fluctuations of trade becomes necessary for international decisions and controlling the world in similar crisis. The COVID-19 pandemic made a new pattern of trade in the world and affected how businesses work and trade with each other. It means that every potential pandemic or any unprecedented event in the world can change the market rules. This research develops a novel model to have a proper estimation of the stock market values with respect to COVID-19 dataset using long short-term memory networks (LSTM).The nature of the features in each pandemic is totally different, thus, prediction results for a pandemic by a specific model cannot be applied to other pandemics. Hence, recognising and extracting the features which affect the pandemic is in the highest priorities. In this study, we develop a framework, providing a better understanding of the features and feature selection. This study is based on a preliminary analysis of such features for enhancing forecasting models' performance against fluctuations in the market.Our forecasts are based on the market value data and COVID-19 pandemic daily time-series data (i.e. the number of new cases). In this study, we selected Gold price as a base for our forecasting task which can be replaced by any other markets. We have applied Convolutional Neural Networks (CNN) LSTM, Vector Out-put Sequence LSTM, Bidirectional LSTM, and Encoder-Decoder LSTM on our dataset and our results achieved an MSE of 6.0e-4, 8.0e-4, and 2.0e-3 on the validation set respectfully for one day, two days, and 30 days predictions in advance which is outperforming other proposed method in the literature.

Factors Influencing International Institutional Investments

2020 ◽  
pp. 195-212
Author(s):  
Akriti Gupta ◽  
Gurpreet Kaur ◽  
Mahesh Sarva
Keyword(s):  
Financial Market ◽  
Time Series Data ◽  
Trade Openness ◽  
Global Market ◽  
Series Data ◽  
Institutional Investment ◽  
Flow Of Funds ◽  
The World ◽  
International Investors ◽  
Financial Market Risk

At the turn of the 21st century, globalization of developed and developing countries in the world witnessed institutional inflows from international investors which became the main characteristic of global capital markets. The current research has assessed time-series data from 2000 to 2017 to understand how the different elements that have influenced the foreign institutional investments and helped India become a global market for such investors. The results revealed that political risk, financial market development, trade openness of the country, size of the economy, and rate of return on investment are the important determinants in attracting foreign institutional investments in India. The chapter also found economic risk and financial market risk played an insignificant role in determining foreign institutional investment in India. The findings of the research help the present government and market regulators to introduce policies aimed at increasing the flow of funds from international institutional investors.

Income and Price Elasticities in India's Trade

1994 ◽  
Vol 19 (2) ◽  
pp. 13-20
Author(s):  
G S Gupta ◽  
H Keshava
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Individual Country ◽  
Price Elasticities ◽  
The World ◽  
Annual Time Series ◽  
Income And Price Elasticities ◽  
Annual Time

This article by G S Gupta and H Keshava estimates the export and import functions for India both at the aggregate (rest of the world) as well as the important individual country levels using annual time series data for the period 1960-61 through 1990-91.

scholarly journals A Deep Learning Based Method for Forecasting Gold Price with Respect to Pandemics

2020 ◽  
Author(s):  
Mahtab Mohtasham Khani ◽  
Sahand Vahidnia ◽  
Alireza Abbasi
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Specific Model ◽  
Gold Price ◽  
Series Data ◽  
Trade Relations ◽  
Market Values ◽  
The World ◽  
Proper Estimation ◽  
Validation Set

Abstract The spread of COVID-19 in the world had a devastating impact on the world economy, trade relations, and globalization. As the pandemic advances and new potential pandemics are on the horizon, a precise analysis of recent fluctuations of trade becomes necessary for international decisions and controlling the world in a similar crisis. The COVID-19 pandemic made a new pattern of trade in the world and affected how businesses work and trade with each other. It means that every potential pandemic or any unprecedented event in the world can change the market rules. This research develops a novel model to have a proper estimation of the stock market values with respect to the COVID-19 dataset using long short-term memory networks (LSTM).The nature of the features in each pandemic is totally different, thus, prediction results for a pandemic by a specific model cannot be applied to other pandemics. Hence, recognizing and extracting the features which affect the pandemic is the highest priority. In this study, we develop a framework, providing a better understanding of the features and feature selection. This study is based on a preliminary analysis of such features for enhancing forecasting models' performance against fluctuations in the market.Our forecasts are based on the market value data and COVID-19 pandemic daily time-series data (i.e. the number of new cases). In this study, we selected Gold price as a base for our forecasting task which can be replaced by any other markets. We have applied Convolutional Neural Networks (CNN) LSTM, Vector Output Sequence LSTM, Bidirectional LSTM, and Encoder-Decoder LSTM on our dataset, and our results achieved an MSE of 6.0e-4, 8.0e-4, and 2.0e-3 on the validation set respectfully for one day, two days, and 30 days predictions in advance which are outperforming other proposed method in the literature.

scholarly journals GLOBAL MARKET OF PETROLEUM PRODUCTS: CURRENT STATE AND PERSPECTIVES OF DEVELOPMENT

2020 ◽  
pp. 27-39
Author(s):  
K. Bezugla ◽  
N. Kostyuchenko
Keyword(s):  
New Technologies ◽  
Time Series Data ◽  
Supply And Demand ◽  
Price Volatility ◽  
Petroleum Products ◽  
Global Market ◽  
Series Data ◽  
Total Output ◽  
Petroleum Production ◽  
The World

The paper is devoted to the peculiarities and perspectives of the global petroleum market development. The peculiarities of supply and demand formation at the global market of petroleum products are investigated in the article. The balance of supply and demand at the petroleum market is determined. The paper outlines the peculiarities of pricing for petroleum products. The dynamics of price changes on the global petroleum market in the period of 2010-2020 is studied. The conclusion was made that there is a price volatility on the global petroleum market. An analysis of the dynamics and structure of the world petroleum production by regions revealed that the total output of oil has increased due to the development of new technologies and due to the increased efficiency of petroleum production. The performed forecasting made it possible to conclude that petroleum price is expected to increase in the coming two periods. That will allow to establish a balance between supply and demand at the petroleum products’ market. Accordingly, the equalization of supply and demand for petroleum products is forecasted (even despite the crisis in the world). The econometric method of economic analysis was applied in the paper. The authors constructed an additive model for time series data to predict the dynamics of prices on the global market of petroleum products. The model was designed based on 16 observations in the period of October 2016 – July 2020.

scholarly journals A Comparison of World and Domestic Price Volatilities of Oilseeds: The Ethiopian Case

2015 ◽  
Vol 7 (2(J)) ◽  
pp. 145-161
Author(s):  
Zerihun G. Kelbore
Keyword(s):  
Poverty Reduction ◽  
Time Series Data ◽  
Market Price ◽  
Price Volatility ◽  
World Market ◽  
Domestic Market ◽  
Series Data ◽  
The World ◽  
Price Risks ◽  
The Government

This study investigates and compares oilseeds price volatilities in the world market and the Ethiopian market. It uses a monthly time series data on oilseeds from February 1999 to December 2012; and analyses price volatilities using unconditional method (standard deviation) and conditional method (GARCH). The results indicate that oilseeds prices are more volatile, but not persistent, in the domestic market than the world market. The magnitude of the influence of the news about past volatility (innovations) is higher in the domestic market for Rapeseed and in the World market for Linseed. However, in both markets there is a problem of volatility clustering. The study also identified that due to the financial crisis the world market price volatilities surpassed and/or paralleled the higher domestic oilseeds price volatilities. The higher domestic oilseeds price volatility may imply that the price risks are high in the domestic oilseeds market. As extreme price volatility influences farmers` production decision, they may opt to other less risky, low-value and less profitable crop varieties. The implications of such retreat is that it may keep the farmers in the traditional farming and impede their transformation to the high value crops, and results in lower income hindering the poverty reduction efforts of the government. This is more important to consider today than was before, because measures undertaken to reduce poverty must bring sustainable change in the lives of the rural poor. For this reason, agricultural policies that enable farmers cope with price risks and enhance their productivity are crucial.

Impact of Global Financial Crisis on FDI Inflows in India and Bangladesh

2017 ◽  
Vol 6 (1) ◽  
pp. 34-63
Author(s):  
Rajib Bhattacharyya
Keyword(s):  
Financial Crisis ◽  
Time Series Data ◽  
Global Financial Crisis ◽  
Structural Break ◽  
High Growth ◽  
High Growth Rate ◽  
Series Data ◽  
Fdi Inflows ◽  
Smooth Flow ◽  
The World

The investment climate in India and Bangladesh has undoubtedly become friendlier and investing in these countries has been an attractive proposition today than in earlier years. According to A.T. Kearney's FDI Confidence Index (2014) India ranks 7th on the basis of FDI inflows in the world while Bangladesh ranks 3rdamong SAARC countries. The present analysis attempts to show that though the global financial crisis (2008) had adversely impacted the growth in GDP and employment opportunities and FDI flows throughout the world, India and Bangladesh both had shown considerable resilience to the global economic crisis by maintaining a high growth rate during this period in the world. It highlights the changes the policy regimes in the two countries. It also tries to examine empirically, using secondary time series data, the amount of FDI inflows, component-wise and sector-wise break-up in FDI inflows in both countries during the pre and post-crisis era, based on Exogenous Structural Break Model. The empirical analysis clearly reveals both FDI and FDI-GDP ratio exhibits stationary trend in India while they are difference stationary in case of Bangladesh. It also focuses on the crisis management policies in the two nations for smooth flow of FDI in the long run.

Jaya Spider Monkey Optimization-driven Deep Convolutional LSTM for the prediction of COVID’19

2020 ◽  
Vol 16 (4) ◽  
Author(s):  
Satish Chander ◽  
Vijaya Padmanabha ◽  
Joseph Mani
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Time Series Data ◽  
Moving Average ◽  
Spider Monkey ◽  
World Health ◽  
Series Data ◽  
Mean Square ◽  
The World ◽  
Spider Monkey Optimization

AbstractCOVID’19 is an emerging disease and the precise epidemiological profile does not exist in the world. Hence, the COVID’19 outbreak is treated as a Public Health Emergency of the International Concern by the World Health Organization (WHO). Hence, an effective and optimal prediction of COVID’19 mechanism, named Jaya Spider Monkey Optimization-based Deep Convolutional long short-term classifier (JayaSMO-based Deep ConvLSTM) is proposed in this research to predict the rate of confirmed, death, and recovered cases from the time series data. The proposed COVID’19 prediction method uses the COVID’19 data, which is the trending domain of research at the current era of fighting the COVID’19 attacks thereby, to reduce the death toll. However, the proposed JayaSMO algorithm is designed by integrating the Spider Monkey Optimization (SMO) with the Jaya algorithm, respectively. The Deep ConvLSTM classifier facilitates to predict the COVID’19 from the time series data based on the fitness function. Besides, the technical indicators, such as Relative Strength Index (RSI), Rate of Change (ROCR), Exponential Moving Average (EMA), Williams %R, Double Exponential Moving Average (DEMA), and Stochastic %K, are extracted effectively for further processing. Thus, the resulted output of the proposed JayaSMO-based Deep ConvLSTM is employed for COVID’19 prediction. Moreover, the developed model obtained the better performance using the metrics, like Mean Square Error (MSE), and Root Mean Square Error (RMSE) by considering confirmed, death, and the recovered cases of COVID’19 for China and Oman. Thus, the proposed JayaSMO-based Deep ConvLSTM showed improved results with a minimal MSE of 1.791, and the minimal RMSE of 1.338 based on confirmed cases in Oman. In addition, the developed model achieved the death cases with the values of 1.609, and 1.268 for MSE and RMSE, whereas the MSE and the RMSE value of 1.945, and 1.394 is achieved by the developed model using recovered cases in China.

scholarly journals Breaking botnets: A quantitative analysis of individual, technical, isolationist, and multilateral approaches to cybersecurity

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Justin K Haner ◽  
Robert K Knake
Keyword(s):  
Time Series Data ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Power Grids ◽  
Series Data ◽  
The Internet ◽  
The World ◽  
Internet Freedom ◽  
Belt And Road ◽  
Technical Solutions

Abstract Malicious networks of botnets continue to grow in strength as millions of new users and devices connect to the internet each day, many becoming unsuspectingly complicit in cyber-attacks or unwitting accomplices to cybercrimes. Both states and nonstate actors use botnets to surreptitiously control the combined computing power of infected devices to engage in espionage, hacking, and to carry out distributed denial of service attacks to disable internet-connected targets from businesses and banks to power grids and electronic voting systems. Although cybersecurity professionals have established a variety of best practices to fight botnets, many important questions remain concerning why levels of botnet infections differ sharply from country to country, as relatively little empirical testing has been done to establish which policies and approaches to cybersecurity are actually the most effective. Using newly available time-series data on botnets, this article outlines and tests the conventionally held beliefs and cybersecurity strategies at every level—individual, technical, isolationist, and multilateral. This study finds that wealthier countries are more vulnerable than less wealthy countries; that technical solutions, including patching software, preventing spoofing, and securing servers, consistently outperform attempts to educate citizens about cybersecurity; and that countries which favor digital isolation and restrictions on internet freedom are not actually better protected than those who embrace digital freedom and multilateral approaches to cybersecurity. This latter finding is of particular importance as China’s attempts to fundamentally reshape the internet via the “Digital Silk Road” component of the Belt and Road Initiative will actually end up making both China and the world less secure. Due to the interconnected nature of threats in cyberspace, states should instead embrace multilateral, technical solutions to better govern this global common and increase cybersecurity around the world.

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