scholarly journals Trade Competitiveness of Pakistan's Fruits and Vegetables in World Market

2020 ◽  
Vol V (IV) ◽  
pp. 135-143
Author(s):  
Hira Manzoor ◽  
Muhammad Safyan ◽  
Fabia Manzoor
Keyword(s):  
Comparative Advantage ◽  
Fruits And Vegetables ◽  
Time Series Data ◽  
World Market ◽  
Series Data ◽  
Vegetable Crops ◽  
Data Set ◽  
Trade Competitiveness ◽  
Vegetables And Fruits ◽  
Analytical Tools

Fruit and vegetable crops are a priority in agriculture by virtue of their vast potential in improving the socio-economic conditions of the country. To investigate the international competitiveness by analyzing the comparative and competitive advantage of vegetables and fruits from Pakistan, Revealed Comparative Advantage (RCA), Relative Export Advantage (RXA), Relative Import Advantage( RMA), Relative Trade Advantage (RTA) and Laffey Index used as analytical tools. For this purpose, time-series data set from the International Trade Center from 2011-2019. Findings revealed that Pakistan maintained a comparative advantage and competitiveness in imports of fruits while disadvantaging vegetables. Even though Pakistan has export competitiveness over its rivals but is still importing a huge amount of fruits and vegetables. To gain better competitiveness in exports of horticultural products and to reduce imports, it is important to rethink the trade policies of Pakistan and invest in the research and development sector.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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.

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

scholarly journals Export Competitiveness Analysis of Creative Industries in the European Union

2020 ◽  
Vol 17 (1) ◽  
pp. 28-37
Author(s):  
Deimantė Krisiukėnienė ◽  
Vaida Pilinkienė
Keyword(s):  
International Trade ◽  
Comparative Advantage ◽  
Theoretical Background ◽  
Creative Industries ◽  
Research Purpose ◽  
The European Union ◽  
Revealed Comparative Advantage ◽  
Data Set ◽  
Trade Competitiveness ◽  
The Eu

AbstractResearch purpose. The research purpose is to assess and compare the competitiveness of the EU creative industries’ export.Design/Methodology/Approach. The article is organised as follows: Section 1 presents a short theoretical conception of creative industries; Section 2 presents the theoretical background of trade competitiveness indices; Section 3 introduces the research data set, method and variables; Section 4 discusses the results of the revealed comparative advantage index analysis; and the final section presents the conclusions of the research. It should be noted that the research does not cover all possible factors underlying the differences in the external sector performance and thus may need to be complemented with country-specific analysis as warranted. Methods of the research include theoretical review and analysis, evaluation of comparative advantage indices and clustering.Findings. The analysis revealed that the EU countries may gain competitiveness because of the globalisation effects and the development of creative industries. The increase in the revealed comparative advantage (RCA) index during the period 2004–2017 shows rising EU international trade specialisation in creative industries. According to dynamic RCA index results, France, Poland, Slovakia, Slovenia and Spain has competitive advantage in creative industries sectors and could be specified as ‘rising stars’ according to dynamic of their export.Originality/Value/Practical implications. A creative industries analysis is becoming increasingly relevant in scientific research. Fast globalisation growth affects the processes in which closed economies together with their specific sectors are no longer competitive in the market because productivity of countries as well as particular economic sectors depends on international trade liberalisation, technology and innovation. Scientific literature, nevertheless, contains a gap in the area of international trade competitiveness research in creative industries sector.

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.  

Economic growth, Industrialization, Trade, Electricity production and Carbon dioxide emissions: Evidence from Ghana

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Kingsley Appiah ◽  
Rhoda Appah ◽  
Oware Kofi Mintah ◽  
Benjamin Yeboah
Keyword(s):  
Economic Growth ◽  
Carbon Dioxide ◽  
Carbon Dioxide Emissions ◽  
Time Series Data ◽  
Electricity Production ◽  
Series Data ◽  
Data Set ◽  
Development Indicators ◽  
Explanatory Variables ◽  
Distributed Lag

Abstract: The study scrutinized correlation between electricity production, trade, economic growth, industrialization and carbon dioxide emissions in Ghana. Our study disaggregated trade into export and import to spell out distinctive and individual variable contribution to emissions in Ghana. In an attempt to investigate, the study used time-series data set of World Development Indicators from 1971 to 2014. By means of Autoregressive Distributed Lag (ARDL) cointegrating technique, study established that variables are co-integrated and have long-run equilibrium relationship. Results of long-term effect of explanatory variables on carbon dioxide emissions indicated that 1% each increase of economic growth and industrialization, will cause an increase of emissions by 16.9% and 79% individually whiles each increase of 1% of electricity production, trade exports, trade imports, will cause a decrease in carbon dioxide emissions by 80.3%, 27.7% and 4.1% correspondingly. In the pursuit of carbon emissions' mitigation and achievement of Sustainable Development Goal (SDG) 13, Ghana need to increase electricity production and trade exports.   

Exploratory Time Series Data Mining by Genetic Clustering

2011 ◽  
pp. 157-178
Author(s):  
T. Warren Liao
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Distance Measures ◽  
Series Data ◽  
Synthetic Control ◽  
Data Set ◽  
Univariate Time Series ◽  
Genetic Clustering ◽  
Data Objects

In this chapter, we present genetic algorithm (GA) based methods developed for clustering univariate time series with equal or unequal length as an exploratory step of data mining. These methods basically implement the k-medoids algorithm. Each chromosome encodes in binary the data objects serving as the k-medoids. To compare their performance, both fixed-parameter and adaptive GAs were used. We first employed the synthetic control chart data set to investigate the performance of three fitness functions, two distance measures, and other GA parameters such as population size, crossover rate, and mutation rate. Two more sets of time series with or without known number of clusters were also experimented: one is the cylinder-bell-funnel data and the other is the novel battle simulation data. The clustering results are presented and discussed.

A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter

2004 ◽  
Vol 91 (3-4) ◽  
pp. 332-344 ◽  
Author(s):  
Jin Chen ◽  
Per. Jönsson ◽  
Masayuki Tamura ◽  
Zhihui Gu ◽  
Bunkei Matsushita ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
High Quality ◽  
Data Set ◽  
Simple Method ◽  
Ndvi Time Series
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