Development Model of Engineering Manufacturing and its Application

2014 ◽  
Vol 1006-1007 ◽  
pp. 386-389
Author(s):  
Ying Hong Yu
Keyword(s):  
Manufacturing Industry ◽  
Time Series Data ◽  
Production Capacity ◽  
Economic Modeling ◽  
Series Data ◽  
High Tech ◽  
Network Training ◽  
Modeling And Forecasting ◽  
Practical Engineering ◽  
Forecasting System

In recent years, with the continuous improvement of production capacity, manufacturing industry restructuring and achieved great results, significantly increased the proportion of high-tech industries, some traditional industries has continued to decline. Manufacturing is the material basis of our national economy and the main industry, which largely determines the level of development of comprehensive national strength. This has brought great difficulties to the economic modeling and forecasting system. This paper presents an improved modeling and forecasting methods, recombinant methods by introducing chain data and add data growth economic indicators in an artificial neural network training, the time series data input window to solve practical engineering problems forecasts.

Dynamic Model and its Application in Economic Forecasting

2014 ◽  
Vol 568-570 ◽  
pp. 1964-1968
Author(s):  
Jing Han Yuan
Keyword(s):  
Time Series Data ◽  
Economic Modeling ◽  
Series Data ◽  
Network Training ◽  
Highly Nonlinear ◽  
Modeling And Forecasting ◽  
Practical Engineering ◽  
Forecasting System ◽  
Artificial Neural Network Training ◽  
Growth Economic

The economic system is an extremely complex system, internal systems affected by many factors, highly nonlinear, time delay and other characteristics. This has brought great difficulties to the economic modeling and forecasting system. This paper presents an improved modeling and forecasting methods, recombinant methods by introducing chain data and add data growth economic indicators in an artificial neural network training, the time series data input window to solve practical engineering problems forecasts.

Implementasi Metode Winter untuk Forecasting Pertumbuhan Jumlah Penduduk: Studi Kasus Wilayah Provinsi NTB

2020 ◽  
Vol 3 (1) ◽  
pp. 51-61
Author(s):  
Syaharuddin ◽  
Abdul Adhiim Rizky ◽  
Lutfi Jauhari ◽  
Siti Fatimah ◽  
Wahyu Ningsih ◽  
...  
Keyword(s):  
Time Series Data ◽  
Optimal Parameter ◽  
Series Data ◽  
Percentage Error ◽  
Average Error ◽  
Ratio Data ◽  
Female Data ◽  
Forecasting System ◽  
Conducting Research ◽  
And Gender

This research aims to analyse the acceleration of population growth based on gender in West Nusa Tenggara Province (NTB) using the Forecasting system by constructing the winter's method in the shape of the Multiple Forecasting System (G-MFS) based on Matlab by calculating the period indicator for accuracy to find time series data in the year 2020-2029. At the simulation stage, researchers used the population and gender ratio data in NTB Province in 2009-2019. The method used in conducting research is to use the winter's method. The evaluation of Forecasting results is done by calculating the average error value using the Mean Absolute Percentage Error (MAPE) method. From this study obtained the most optimal parameter value on male data namely ʌ, β and γ sequential values of 0.9, 0.5 and 0.9 while in female data, the value of ʌ, β and γ respectively, 0.2, 0.1 and 0.5. Then with the value of the parameter obtained MAPE value in male data of 1.7785% and in female data of 0.89034%.

scholarly journals PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA CURAH HUJAN EMPAT KABUPATEN DI PROVINSI JAWA TENGAH

2019 ◽  
Vol 8 (4) ◽  
pp. 418-427
Author(s):  
Eko Siswanto ◽  
Hasbi Yasin ◽  
Sudarno Sudarno
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Space Time ◽  
Series Data ◽  
Inverse Distance Weighted ◽  
Modeling And Forecasting ◽  
Distance Weighted ◽  
The Relationship ◽  
Inverse Distance

In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling volume of rainfall four locations in Jepara Regency, Kudus Regency, Pati Regency, and Grobogan Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR (11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on rainfall Pati Regency influenced by the rainfall in other regencies. Keywords: GSTAR, RMSE, Rainfall

scholarly journals Untapped export opportunities of Serbian economy after a decade of investment and export based growth model

2021 ◽  
Vol 69 (3-4) ◽  
pp. 273-288
Author(s):  
Jasna Atanasijević ◽  
Duško Vasiljević ◽  
Zoran Nikolić ◽  
Olivera Pavlović
Keyword(s):  
Manufacturing Industry ◽  
Production Capacity ◽  
Electrical Equipment ◽  
Low Complexity ◽  
High Tech ◽  
Embedded Devices ◽  
Financial Reports ◽  
Economic Complexity ◽  
Transfer Of Technology ◽  
Limited Scale

Relying on the economic complexity and product space approach developed by Hidalgo and Haussmann [21], and using trade data, exporters' financial reports and available macroeconomic statistics, we try to assess the degree of transformation of structure and production potential of the Serbian economy over the last decade. We argue that although the overall economic complexity, as a decent predictor of higher economic growth, did slightly improve over the observed period, there is still large untapped potential in local knowledge and know-how. FDI inflow into manufacturing industry, as the most important factor of the transformation of the production structure and size of the economy, has contributed to growth in employment and export, improving the macro stability. On the other side, its contribution to the higher growth outlook by improving the production capacity was limited as FDI inflow has been directed mostly into low and medium-low technology industries with low complexity products. Moreover, it seems that the vertical spillover through linkages with local suppliers and transfer of technology, knowledge and practices could also be larger. In the same period, some positive developments of limited scale yet are reflected in emergence of a certain number of high-tech industries' products with high complexity, most likely produced by SMEs, such as electrical equipment, lighting, various software embedded devices, etc.

scholarly journals Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 349-356
Author(s):  
J. HAZARIKA ◽  
B. PATHAK ◽  
A. N. PATOWARY
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Demand Management ◽  
Arima Model ◽  
Monthly Rainfall ◽  
Series Data ◽  
Data Set ◽  
Modeling And Forecasting ◽  
Moving Average Model

Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model introduced by Box and Jenkins. In this study, an attempt has been made to use Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken from Dibrugarh for the period of 1980- 2014 with a total of 420 points.  We investigated and found that ARIMA (0, 0, 0) (0, 1, 1)12 model is suitable for the given data set. As such this model can be used to forecast the pattern of monthly rainfall for the upcoming years, which can help the decision makers to establish priorities in terms of agricultural, flood, water demand management etc.  

An intraregional economic analysis of production structure and factor demand in major Canadian softwood lumber producing regions

1988 ◽  
Vol 18 (8) ◽  
pp. 1036-1048 ◽  
Author(s):  
J. K. Meil ◽  
J. C. Nautiyal
Keyword(s):  
Technical Change ◽  
Time Series Data ◽  
Decomposition Analysis ◽  
Production Capacity ◽  
Series Data ◽  
Variable Cost ◽  
Production Structure ◽  
Cross Sectional ◽  
Behaviour Factor ◽  
Factor Demand

Cross-sectional time-series data were employed to estimate four intraregional models of production structure and factor demand over the time period 1968–1984. Lumber, tie, and pulp chip information was incorporated into the restricted, single-output, variable cost transcendental logarithmic function. Results indicate that aggregate sectoral studies do not adequately reflect regional production behaviour in the industry. Additional tests for aggregation bias demonstrated that different mill sizes within a region also portray differing production behaviour. Factor demand decomposition analysis indicated that demand for production inputs is not static, but is governed by offsetting dynamic effects. With few exceptions, all mills across regions exemplify material- and energy-using and labour-saving biases in technical change. Larger mills consistenly registered the greatest labour-saving technical change, which countered their lack of attaining significantly large cost-reducing scale economies. Mid-sized mills consistently exhibited the largest returns to scale. The data suggest that small mills are leaving the industry in some regions and production capacity is becoming concentrated in the larger mills.

scholarly journals Environmental Pollution Effects of Regional Industrial Transfer Illustrated with Jiangsu, China

2021 ◽  
Vol 13 (21) ◽  
pp. 12128
Author(s):  
Guangxiong Mao ◽  
Wei Jin ◽  
Ying Zhu ◽  
Yanjun Mao ◽  
Wei-Ling Hsu ◽  
...  
Keyword(s):  
Environmental Pollution ◽  
Manufacturing Industry ◽  
Time Series Data ◽  
Estimation Method ◽  
Raw Material ◽  
Series Data ◽  
Pollution Load ◽  
Pollution Effects ◽  
Industrial Transfer ◽  
Southern Jiangsu

Industrial transfer is reshaping the geographic layout of industries and facilitating the transfer and spread of environmental pollution. This study employs the pollution transfer estimation method to discuss the environmental effect of industrial transfer. By compiling statistics on industries of a certain scale according to time-series data, the researchers compute the pollution load generated by industrial transfer and the difference in pollution emissions for each region and industry. Through the constructed evaluation model, the empirical scope is Jiangsu, which is the most developed industry in China. The results reveal that there is an apparent spatial hierarchy among the transferred industries in Jiangsu. Most industries transfer from the southern Jiangsu region toward the central Jiangsu and northern Jiangsu regions. Environmental pollution is redistributed among prefecture-level cities because of intercity industrial transfer; the spatial characteristics of pollution exhibit a notable hierarchical pattern. Furthermore, the transferred pollution load differs considerably between industries. The textile industry and chemical raw material and chemical product industry are mainly transferred toward the Central Jiangsu and Northern Jiangsu regions, whereas the papermaking and paper product manufacturing industry is primarily redistributed to the Southern Jiangsu region. The empirical results can serve as a reference for analyzing the environmental pollution effects of regional industrial transfer.

scholarly journals A Time-Varying Coupling Analysis of Expressway Traffic Volume and Manufacturing PMI

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shuo Sun ◽  
Mingchen Gu ◽  
Yingping Wang ◽  
Rongjie Lin ◽  
Lifeng Xing ◽  
...  
Keyword(s):  
Manufacturing Industry ◽  
Time Series Data ◽  
Traffic Volume ◽  
Series Data ◽  
Time Varying ◽  
Truck Traffic ◽  
Positive Interaction ◽  
Vector Autoregressive ◽  
Coupling Relationship ◽  
Johansen Cointegration Test

This study investigates the time-varying coupling relationship between expressway traffic volume and manufacturing purchasing manager index (PMI). First, for the traffic volume and manufacturing PMI time-series data, unit root stability test and Johansen cointegration test are applied to determine the stability of single sequence and the long-term stable correlation between variables, respectively. Then, a time-varying vector autoregressive model (TVP-VAR) is developed to quantify the time-varying correlation between variables. The time-varying parameters of TVP-VAR are estimated using the Markov chain Monte Carlo (MCMC) theory. Finally, the model is validated using examples from China. In the numeric example, three variables, i.e., expressway car traffic volume, expressway truck traffic volume, and manufacturing PMI, are selected for analysis. Results show that there is a positive interaction between expressway traffic volume (both car and truck) and manufacturing PMI. Express traffic volume slowly promotes the development of manufacturing industry. However, with the reform policy of road freight structure in China, the promotion effect of truck traffic on manufacturing PMI in the past two years has decreased significantly. Moreover, as affected by the China demand-led economic development model in recent years, the stimulus effect of manufacturing PMI on expressway passenger traffic volume has increased year by year. And, while the expressway freight structure remains stable, truck traffic volume is hardly affected by fluctuations in manufacturing PMI. These research results are helpful for policy makers to understand the time-varying coupling relationship between expressway traffic volume and manufacturing development and finally to improve the expressway management level.

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