scholarly journals A Synchronous Prediction Model Based on Multi-Channel CNN with Moving Window for Coal and Electricity Consumption in Cement Calcination Process

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4284
Author(s):  
Xin Shi ◽  
Gaolu Huang ◽  
Xiaochen Hao ◽  
Yue Yang ◽  
Ze Li
Keyword(s):  
Energy Consumption ◽  
Time Series Data ◽  
Production Optimization ◽  
Cement Industry ◽  
Channel Structure ◽  
Series Data ◽  
Time Lags ◽  
Moving Window ◽  
Coal Consumption ◽  
The Impact

The precision and reliability of the synchronous prediction of multi energy consumption indicators such as electricity and coal consumption are important for the production optimization of industrial processes (e.g., in the cement industry) due to the deficiency of the coupling relationship of the two indicators while forecasting separately. However, the time lags, coupling, and uncertainties of production variables lead to the difficulty of multi-indicator synchronous prediction. In this paper, a data driven forecast approach combining moving window and multi-channel convolutional neural networks (MWMC-CNN) was proposed to predict electricity and coal consumption synchronously, in which the moving window was designed to extract the time-varying delay feature of the time series data to overcome its impact on energy consumption prediction, and the multi-channel structure was designed to reduce the impact of the redundant parameters between weakly correlated variables of energy prediction. The experimental results implemented by the actual raw data of the cement plant demonstrate that the proposed MWMC-CNN structure has a better performance than without the combination structure of the moving window multi-channel with convolutional neural network.

scholarly journals The Impact of CO2 Emissions on the GDP per Capita, Employment Rate and Energy Consumption of China, Korea and Japan

2021 ◽  
Vol 6 (11) ◽  
pp. 315-333
Author(s):  
Allieah A. Mendoza ◽  
Kirby Duane Garret T. Reyes ◽  
Pauline Antonette D. Soriano ◽  
Ronaldo Cabauatan
Keyword(s):  
Energy Consumption ◽  
Co2 Emissions ◽  
Time Series Data ◽  
Gdp Per Capita ◽  
Series Data ◽  
Kuznets Curve ◽  
Total Employment ◽  
Cointegration Tests ◽  
Per Capita ◽  
The Impact

This paper aims to investigate the relationship between CO2 Emissions and GDP per capita of three East Asian countries (China, Japan, and South Korea). The Environmental Kuznets Curve hypothesis and its possible implications to the implementation of the Kyoto Protocol Agreement will be tested. The independent variables Employment and Energy consumption will be used as control variables. Multiple regression analysis and cointegration tests will be used on time series data of Japan, Korea, and China that is obtained from the World Bank database. GDP per capita is measured in constant 2010 US$, CO2 emission in kt, Employment in the ratio of total employment to total population aged 15 and above, and Energy Consumption in annual kWh per capita.

The Impact of Unionization on Productivity: A Case Study

ILR Review ◽  
1980 ◽  
Vol 33 (4) ◽  
pp. 451-469 ◽  
Author(s):  
Kim B. Clark
Keyword(s):  
Time Series Data ◽  
Management Practice ◽  
Cement Industry ◽  
Series Data ◽  
Use Of Time ◽  
Management Personnel ◽  
Union Policy ◽  
Union Impact ◽  
The Impact ◽  
Productivity Gains

This study examines the effect of unionization on productivity through the use of time-series data on selected establishments in the U.S. cement industry. The analysis combines statistical estimation of the union impact and interviews with union and management officials to forge a link between econometric estimation and the traditional institutional analysis of union policy and management practice. The econometric analysis deals primarily with the problem of controlling for interfirm differences in variables such as the quality of management and also for the possible union impact on labor quality. The case studies are designed to show the specific ways in which unionization affects productivity. The empirical results indicate that unionization leads to productivity gains, deriving in large part from a series of extensive changes in management personnel and procedures.

scholarly journals DETERMINING THE ROLE OF ENERGY CONSUMPTION AND AIR POLLUTION ON PAKISTAN’S ECONOMIC GROWTH: APPLICATION OF AUTOREGRESSIVE DISTRIBUTIVE LAG MODEL (ARDL)

2020 ◽  
Vol 7 (3) ◽  
pp. 189-204
Author(s):  
Baseerat Sultana ◽  
Abdul Mansoor
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Electric Power ◽  
Co2 Emission ◽  
Nuclear Energy ◽  
Time Series Data ◽  
High Mass ◽  
Series Data ◽  
Long Run ◽  
The Impact

Abstract: The goals of the study are to observe the impact of energy consumption, CO2 emission on Pakistan’s economic growth by using and annual time series data from 1980 to 2016.  The Auto regressive lag distributive (ARDL) model is applied to find out the correlation between the variables. The short run elasticity shows that CO2 emission, fossil fuel and hydro energy consumption decreases Pakistan’s economic growth, while nuclear energy consumption and electric power consumption substantially increases GDP per capita of the country. In the long run, nuclear energy consumption and electric power generation support country’s economic growth, which need more friendly environmental policies to reduce high mass carbon emissions in a country.  

Impact of exchange rate and oil price on the yield of sovereign bond and sukuk: Evidence from Malaysian capital market

2017 ◽  
Vol 5 (4) ◽  
pp. 27
Author(s):  
Huda Arshad ◽  
Ruhaini Muda ◽  
Ismah Osman
Keyword(s):  
Exchange Rate ◽  
Capital Market ◽  
Time Series Data ◽  
Oil Prices ◽  
Oil Price ◽  
Series Data ◽  
Sovereign Bond ◽  
Long Run ◽  
Short Run ◽  
The Impact

This study analyses the impact of exchange rate and oil prices on the yield of sovereign bond and sukuk for Malaysian capital market. This study aims to ascertain the effect of weakening Malaysian Ringgit and declining of crude oil price on the fixed income investors in the emerging capital market. This study utilises daily time series data of Malaysian exchange rate, oil price and the yield of Malaysian sovereign bond and sukuk from year 2006 until 2015. The findings show that the weakening of exchange rate and oil prices contribute different impacts in the short and long run. In the short run, the exchange rate and oil prices does not have a direct relation with the yield of sovereign bond and sukuk. However, in the long run, the result reveals that there is a significant relationship between exchange rate and oil prices on the yield of sovereign bond and sukuk. It is evident that only a unidirectional causality relation is present between exchange rate and oil price towards selected yield of Malaysian sovereign bond and sukuk. This study provides numerical and empirical insights on issues relating to capital market that supports public authorities and private institutions on their decision and policymaking process.

Transport infrastructure of the Rostov Oblast: Problems and prospects for development in the context of national projects implementation

2020 ◽  
Vol 19 (6) ◽  
pp. 1015-1034
Author(s):  
O.Yu. Patrakeeva
Keyword(s):  
Time Series Data ◽  
Mutual Influence ◽  
Rostov Oblast ◽  
Transport Infrastructure ◽  
Series Data ◽  
Cointegration Tests ◽  
Level Of Economic Development ◽  
Short Run ◽  
The Government ◽  
The Impact

Subject. The paper considers national projects in the field of transport infrastructure, i.e. Safe and High-quality Roads and Comprehensive Plan for Modernization and Expansion of Trunk Infrastructure, and the specifics of their implementation in the Rostov Oblast. Objectives. The aim is to conduct a statistical assessment of the impact of transport infrastructure on the region’s economic performance and define prospects for and risks of the implementation of national infrastructure projects in conditions of a shrinking economy. Methods. I use available statistics and apply methods and approaches with time-series data, namely stationarity and cointegration tests, vector autoregression models. Results. The level of economic development has an impact on transport infrastructure in the short run. However, the mutual influence has not been statistically confirmed. The paper revealed that investments in the sphere of transport reduce risk of accidents on the roads of the Rostov Oblast. Improving the quality of roads with high traffic flow by reducing investments in the maintenance of subsidiary roads enables to decrease accident rate on the whole. Conclusions. In conditions of economy shrinking caused by the complex epidemiological situation and measures aimed at minimizing the spread of coronavirus, it is crucial to create a solid foundation for further economic recovery. At the government level, it is decided to continue implementing national projects as significant tools for recovery growth.

A Macroeconomic Analysis of Agricultural Sector in Nigeria

2019 ◽  
Vol 5 (1) ◽  
pp. 18-25
Author(s):  
Isah Funtua Abubakar ◽  
Umar Bambale Ibrahim
Keyword(s):  
Value Chain ◽  
Crop Production ◽  
Agricultural Sector ◽  
Time Series Data ◽  
Tourism Industry ◽  
Series Data ◽  
Size Analysis ◽  
The Real ◽  
Real Gdp ◽  
The Impact

This paper attempts to study the Nigerian agriculture industry as a panacea to growth as well as an anchor to the diversification agenda of the present government. To do this, the time series data of the four agriculture subsectors of crop production, livestock, forestry and fishery were analysed as stimulus to the Real GDP from 1981-2016 in order to explicate the individual contributions of the subsectors to the RGDP in order to guide the policy thrust on diversification. Using the Johansen approach to cointegration, all the variables were found to be cointegrated. With the exception of the forestry subsector, all the three subsectors were seen to have impacted on the real GDP at varying degrees during the time under review. The crop production subsector has the highest impact, however, taking size-by-size analysis, the livestock subsector could be of much importance due to its ability to retain its value chain and high investment returns particularly in poultry. Therefore, it is recommended that, the government should intensify efforts to retain the value chain in the crop production subsector, in order to harness its potentials optimally through the encouragement of the establishment of agriculture cottage industries. Secondly, the livestock subsector is found to be the most rapidly growing and commercialized subsector. Therefore, it should be the prime subsector to hinge the diversification agenda naturally. Lastly, the tourism industry which is a source through which the impact of the subsector is channeled to the GDP should be developed, in order to improve the impact of such channel to GDP with the sole objective to resuscitate the forestry subsector.

The Effects of Oil Prices Changes on Output Growth and Inflation: Evidence from Turkey

2013 ◽  
Vol 5 (11) ◽  
pp. 730-739 ◽  
Author(s):  
Pelin ÖGE GÜNEY
Keyword(s):  
Time Series Data ◽  
Output Growth ◽  
Oil Price ◽  
Series Data ◽  
Negative Effects ◽  
Price Changes ◽  
Asymmetric Effects ◽  
Price Increases ◽  
Causality Tests ◽  
The Impact

This paper investigates the effects of oil price changes on output and inflation for the case of Turkey using monthly time series data for the period 1990:1–2012:3. Recent studies suggest that oil price changes may have asymmetric effects on the macroeconomic variables. To account for asymmetric effects, we decompose oil price changes into positive and negative parts following Hamilton (1996). Our results show that while oil price increases have clear negative effects on output growth, the impact of oil price decline is insignificant. Similarly, oil price increases have positive and significant effects on inflation. However, oil price declines have not a significant effect on inflation. The Granger causality tests also support these results.

Does Tax Increment Financing Pass the “But-for” Test in Missouri?

2019 ◽  
Vol 33 (3) ◽  
pp. 187-202
Author(s):  
Ahmed Rachid El-Khattabi ◽  
T. William Lester
Keyword(s):  
Economic Development ◽  
Kansas City ◽  
Time Series Data ◽  
Local Level ◽  
Conclusive Evidence ◽  
Series Data ◽  
Tax Increment Financing ◽  
Data Set ◽  
Development Indicators ◽  
The Impact

The use of tax increment financing (TIF) remains a popular, yet highly controversial, tool among policy makers in their efforts to promote economic development. This study conducts a comprehensive assessment of the effectiveness of Missouri’s TIF program, specifically in Kansas City and St. Louis, in creating economic opportunities. We build a time-series data set starting 1990 through 2012 of detailed employment levels, establishment counts, and sales at the census block-group level to run a set of difference-in-differences with matching estimates for the impact of TIF at the local level. Although we analyze the impact of TIF on a wide set of indicators and across various industry sectors, we find no conclusive evidence that the TIF program in either city has a causal impact on key economic development indicators.

EFFECTS OF TREND AND PERIODICITY ON THE CORRELATION DIMENSION AND THE LYAPUNOV EXPONENTS

2008 ◽  
Vol 18 (12) ◽  
pp. 3679-3687 ◽  
Author(s):  
AYDIN A. CECEN ◽  
CAHIT ERKAL
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Lyapunov Exponents ◽  
Correlation Dimension ◽  
Time Series Data ◽  
Logistic Map ◽  
Model Identification ◽  
Series Data ◽  
Largest Lyapunov Exponent ◽  
The Impact

We present a critical remark on the pitfalls of calculating the correlation dimension and the largest Lyapunov exponent from time series data when trend and periodicity exist. We consider a special case where a time series Zi can be expressed as the sum of two subsystems so that Zi = Xi + Yi and at least one of the subsystems is deterministic. We show that if the trend and periodicity are not properly removed, correlation dimension and Lyapunov exponent estimations yield misleading results, which can severely compromise the results of diagnostic tests and model identification. We also establish an analytic relationship between the largest Lyapunov exponents of the subsystems and that of the whole system. In addition, the impact of a periodic parameter perturbation on the Lyapunov exponent for the logistic map and the Lorenz system is discussed.

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

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