scholarly journals An Application of the Decision Support Model to Louisiana’s Exports

2018 ◽  
Vol 5 (1) ◽  
pp. 4307-4313
Author(s):  
Bukola B. Oluwade
Keyword(s):  
Time Series ◽  
Decision Support ◽  
Time Series Data ◽  
The United States ◽  
Census Bureau ◽  
The State ◽  
Series Data ◽  
Decision Support Model ◽  
United States Census Bureau ◽  
Support Model

Recognizing the relevance of exportation to the development and growth of many nations, government and business entities- mainly in the Louisiana State, the policymakers and other key stakeholders should be devoting more time to expand its export opportunities for more revenue generation. This current study revolved around the Export Decision Support Model (EDSM) propounded by Viviers and Cuyvers (2012).  The time series data was sourced from the United States Census Bureau (2016) survey on State exports from Louisiana. The objective of this study was to demonstrate the modification of the EDSM for the development of the Louisiana State Exportation. The EDSM is designed and modified purposely for the State of Louisiana to enable it identifies the various export opportunities. The study used a time-series data across a variety of export commodities and import countries available in the State of Louisiana from 2013 to 2016. Based on the data from the U.S. Census Bureau and the International trade data between 2013 and 2016, it was underscored that the State’s gross export accounted for 80.4% , while the net export estimated around 8.8% of the Louisiana’s GDP in 2016 with a strong focus across Asia, and Europe. The study recommend that policymakers should pay more attention to the prioritize export commodities outline in the study. 

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.

Abstract 19225: Impact of Change in Resuscitation Guidelines on National Out-of-hospital Cardiac Arrest Outcomes: Fulfilled Expectations?

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Shaker M Eid ◽  
Aiham Albaeni ◽  
Rebeca Rios ◽  
May Baydoun ◽  
Bolanle Akinyele ◽  
...  
Keyword(s):  
Time Series ◽  
Cardiac Arrest ◽  
Time Series Data ◽  
Statistical Significance ◽  
Interrupted Time Series ◽  
The United States ◽  
Series Data ◽  
National Database ◽  
Resuscitation Guidelines ◽  
Hospital Cardiac Arrest

Background: The intent of the 5-yearly Resuscitation Guidelines is to improve outcomes. Previous studies have yielded conflicting reports of a beneficial impact of the 2005 guidelines on out-of-hospital cardiac arrest (OHCA) survival. Using a national database, we examined survival before and after the introduction of both the 2005 and 2010 guidelines. Methods: We used the 2000 through 2012 National Inpatient Sample database to select patients ≥18 years admitted to hospitals in the United States with non-traumatic OHCA (ICD-9 CM codes 427.5 & 427.41). A quasi-experimental (interrupted time series) design was used to compare monthly survival trends. Outcomes for OHCA were compared pre- and post- 2005 and 2010 resuscitation guidelines release as follows: 01/2000-09/2005 vs. 10/2005-9/2010 and 10/2005-9/2010 vs. 10/2010-12/2012. Segmented regression analyses of interrupted time series data were performed to examine changes in survival to hospital discharge. Results: For the pre- and post- guidelines periods, 81600, 69139 and 36556 patients respectively survived to hospital admission following OHCA. Subsequent to the release of the 2005 guidelines, there was a statistically significant worsening in survival trends (β= -0.089, 95% CI -0.163 – -0.016, p =0.018) until the release of the 2010 guidelines when a sharp increase in survival was noted which persisted for the period of study (β= 0.054, 95% CI -0.143 – 0.251, p =0.588) but did not achieve statistical significance (Figure). Conclusion: National clinical guidelines developed to impact outcomes must include mechanisms to assess whether benefit actually occurs. The worsening in OHCA survival following the 2005 guidelines is thought provoking but the improvement following the release of the 2010 guidelines is reassuring and worthy of perpetuation.

Widely Applicable Multi-Variate Decision Support Model for Market Trend Analysis and Prediction with Case Study in Retail

2014 ◽  
pp. 989-1018 ◽  
Author(s):  
Leo Mršić
Keyword(s):  
Time Series ◽  
Decision Support ◽  
Bayesian Network ◽  
Real Life ◽  
Heterogeneous Data Source ◽  
Decision Support Model ◽  
Market Trends ◽  
Series Transformation ◽  
Support Model

Chapter explains efficient ways of dealing with business problems of analyzing market environment and market trends under complex circumstances using heterogeneous data source. Under the assumption that used data can be expressed as time series, widely applicable multi variate model is explained together with case study in textile retail. This Chapter includes an overview of research conducted with a brief explanation of approaches and models available today. A widely applicable multi-variate decision support model is presented with advantages, limitations, and several variations for development. The explanation is based on textile retail case study with model wide range of possible applications in perspective. Complex business environment issues are simulated with explanation of several important global trends in textile retail in past seasons. Non-traditional approaches are revised as tools for a better understanding of modern market trends as well as references in relevant literature. A widely applicable multi-variate decision support model and its usage is presented through built stages and simulated. Model concept is based on specific time series transformation method in combination with Bayesian logic and Bayesian network as final business logic layer with front end interface built with open source Bayesian network tool. Explained case study provides one of the most challenging issue in textile retail: market trends seasonal/weather dependence. Separate outcomes for different scenario analysis approaches are presented on real life data from a textile retail chain located in Zagreb, Croatia. Chapter ends with a discussion about similar research’s, wide applicability of presented model with references for future research.

Structural condition assessment using entropy-based time series analysis

2017 ◽  
Vol 28 (14) ◽  
pp. 1941-1956 ◽  
Author(s):  
Mehrisadat Makki Alamdari ◽  
Bijan Samali ◽  
Jianchun Li ◽  
Ye Lu ◽  
Samir Mustapha
Keyword(s):  
Time Series ◽  
State Space ◽  
Structural Damage ◽  
Time Series Data ◽  
National Laboratory ◽  
The State ◽  
Frame Structure ◽  
Series Data ◽  
Symbol Space ◽  
Symbol Sequences

We present a time-series-based algorithm to identify structural damage in the structure. The method is in the context of non-model-based approaches; hence, it eliminates the need of any representative numerical model of the structure to be built. The method starts by partitioning the state space into a finite number of subsets which are mutually exclusive and exhaustive and each subset is identified by a distinct symbol. Partitioning is performed based on a maximum entropy approach which takes into account the sparsity and distribution of information in the time series. After constructing the symbol space, the time series data are uniquely transformed from the state space into the constructed symbol space to create the symbol sequences. Symbol sequences are the simplified abstractions of the complex system and describe the evolution of the system. Each symbol sequence is statistically characterized by its entropy which is obtained based on the probability of occurrence of the symbols in the sequence. As a consequence of damage occurrence, the entropy of the symbol sequences changes; this change is implemented to define a damage indicative feature. The method shows promising results using data from two experimental case studies subject to varying excitation. The first specimen is a reinforced concrete jack arch which replicates one of the major structural components of the Sydney Harbor Bridge and the second specimen is a three-story frame structure model which has been tested at Los Alamos National Laboratory. The method not only could successfully identify the presence of damage but also has potential to localize it.

Time series data mining for demand side decision support

2013 ◽  
Author(s):  
Neha Sengupta ◽  
S Aloka ◽  
Balakrishnan Narayanaswamy ◽  
Hamidah Ismail ◽  
Satyajith Mathew
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Decision Support ◽  
Time Series Data ◽  
Series Data ◽  
Demand Side ◽  
Time Series Data Mining

scholarly journals Analisis Anggaran Pemerintah (APBN dan APBN-P) dalam Perspektif Demokrasi Multipartai dan Koalisi

2016 ◽  
Vol 17 (1) ◽  
pp. 28-43
Author(s):  
Badikenita Sitepu
Keyword(s):  
Time Series ◽  
Political System ◽  
Time Series Data ◽  
The State ◽  
Series Data ◽  
State Budget ◽  
Sector Reform ◽  
Approval Rate ◽  
Budget Analysis ◽  
Financial Sector Reform

Government Budget Analysis in Perspective Democracy Multiparty and CoalitionDemocratic system change in Indonesia resulted in a change of political system and economy of Indonesia. Changes in the political system was also followed by the country’s financial sector reform (or budget) in the process of change towards prosperity residents better. Using time series data from 1982 to 2011, this study found that the coalition is formed to have a positive and significant effect on the level of budget revenues in the state budget approval and the state budget, as well as the approval of the budget at the level of state budget. The Coalition does not have influence on the level of approval signifkan budget on state budget. The number of political parties only aect the level of budget revenues in the state budget approval. The level of tax agreements have a significant impact on the level of state budget approval and the state budget. Revenue budget approval rate has a significant influence on the level of approval of both the state budget expenditures and state budget.Keywords: State Budget; Multiparty System; Coalition; Political Party; Level Tax Agreement AbstrakPerubahan sistem demokrasi di Indonesia berdampak terhadap perubahan sistem politik dan ekonomi Indonesia. Perubahan sistem politik juga diikuti dengan reformasi di bidang keuangan negara (atau anggaran) dalam proses perubahan menuju kemakmuran penduduk yang lebih baik. Dengan menggunakan data time series tahun 1982–2011, penelitian ini menemukan bahwa koalisi yang terbentuk berpengaruh positif dan signifikan terhadap tingkat persetujuan anggaran pendapatan pada APBN dan APBN-P, serta tingkat persetujuan anggaran belanja pada APBN-P. Koalisi tidak berpengaruh signifikan terhadap tingkat persetujuan anggaran belanja pada APBN-P. Jumlah partai politik hanya berpengaruh pada tingkat persetujuan anggaran pendapatan pada APBN. Tingkat persetujuan pajak berpengaruh signifikan terhadap tingkat persetujuan anggaran pada APBN dan APBN-P. Tingkat persetujuan anggaran pendapatan berpengaruh signifikan terhadap tingkat persetujuan anggaran belanja, baik pada APBN maupun APBN-P.

scholarly journals Processing of Rainfall Time Series Data in the State of Rio de Janeiro

2021 ◽  
Vol 4 (4) ◽  
Author(s):  
Givanildo De Gois ◽  
José Francisco De Oliveira-Júnior
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
The State ◽  
Data Consistency ◽  
Quality Data ◽  
Series Data ◽  
Time Variability ◽  
Rainfall Time Series ◽  
Raw Data ◽  
State Data

The goal was to perform the filling, consistency and processing of the rainfall time series data from 1943 to 2013 in five regions of the state. Data were obtained from several sources (ANA, CPRM, INMET, SERLA and LIGHT), totaling 23 stations. The time series (raw data) showed failures that were filled with data from TRMM satellite via 3B43 product, and with the climatological normal from INMET. The 3B43 product was used from 1998 to 2013 and the climatological normal over the 1947- 1997 period. Data were submitted to descriptive and exploratory analysis, parametric tests (Shapiro-Wilks and Bartlett), cluster analysis (CA), and data processing (Box Cox) in the 23 stations. Descriptive analysis of the raw data consistency showed a probability of occurrence above 75% (high time variability). Through the CA, two homogeneous rainfall groups (G1 and G2) were defined. The group G1 and G2 represent 77.01% and 22.99% of the rainfall occurring in SRJ, respectively. Box Cox Processing was effective in stabilizing the normality of the residuals and homogeneity of variance of the monthly rainfall time series of the five regions of the state. Data from 3B43 product and the climatological normal can be used as an alternative source of quality data for gap filling.

scholarly journals Variability and Sources of Output Growth in Major Oilseeds of Rajasthan

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Kailash Chand Bairwa
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Time Series Data ◽  
Output Growth ◽  
The State ◽  
Series Data ◽  
Oilseed Crops ◽  
Significant Growth ◽  
Area Effect ◽  
Relative Contribution

Rajasthan state is the second largest oilseeds producer and land coverage in the country. The share of oilseed crops is scheduled the significant growth in area and output in latest 20 years. Nevertheless, compare to wheat and gram, the growth rate of area and production of several oilseeds is less significant and there exist wide instability in their productivity in scattered part of the state. This study investigates to growth, its contributors and variability in area, production and productivity of major oilseed crops. The study period from 1990-91 to 2019-20 was divided into three sub-periods viz., period-I (1990-91 to 2004-05); period-II (2005-06 to 2019-20) and Overall study Period (1990-91 to 2018-19). Time series data were collected from various public E-sources to compute the growth, instability and decomposition in oilseeds production. It was revealed from the analysis that growth of kharif oilseeds was higher than rabi oilseeds. The highest instability (31.78) in production and productivity was reported in period-I for kharif oilseeds. In case of relative contribution, the area effect (416.85) and yield effects (211.10) were more effective in production of taramira and sesame crops, respectively. This analysis suggested that during period –I and II area effect was dominant in changing output of taramira and rapeseed-mustard.

scholarly journals Automated evaluation of agricultural damage using UAV survey

2018 ◽  
Vol 10 (1) ◽  
pp. 20-30 ◽  
Author(s):  
Daniel Stojcsics ◽  
Zsolt Domozi ◽  
András Molnár
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
The State ◽  
Series Data ◽  
Automated Evaluation ◽  
Gis Data ◽  
Automated Process ◽  
Post Production ◽  
Comparison Of The Results

Abstract In the last decade, the rate of the industrial usage of fixed-wing and blended wing aircraft has increased. A 1–2-km2 area can be surveyed by such a drone within 30 to 60 minutes, without any special infrastructure, and this can be repeated at any time. This provides an opportunity to conduct automatized surveys and time series data testing, which can be used as a basis to decide specific processes. The state and the development of the plants can be monitored as well as the spread of pests and the efficiency of the procedures that protect against them. During the surveys, thousands of images are taken of the area, which can be converted to a georeferenced large-sized map within 20 to 40 hours, including post-production and a resolution varying from 0.01 to 0.1 cm/pixel. The paper provides a solution to the industrial post-production of these high-quantity data, in which a deep learning-based automated process using Matlab is presented, including a comparison of the results to the GIS data.

scholarly journals Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data

2021 ◽  
Vol 10 (2) ◽  
pp. 870-878
Author(s):  
Zainuddin Z. ◽  
P. Akhir E. A. ◽  
Hasan M. H.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
The State ◽  
Series Data ◽  
Machine Failure ◽  
Gated Recurrent Unit

Time series data often involves big size environment that lead to high dimensionality problem. Many industries are generating time series data that continuously update each second. The arising of machine learning may help in managing the data. It can forecast future instance while handling large data issues. Forecasting is related to predicting task of an upcoming event to avoid any circumstances happen in current environment. It helps those sectors such as production to foresee the state of machine in line with saving the cost from sudden breakdown as unplanned machine failure can disrupt the operation and loss up to millions. Thus, this paper offers a deep learning algorithm named recurrent neural network-gated recurrent unit (RNN-GRU) to forecast the state of machines producing the time series data in an oil and gas sector. RNN-GRU is an affiliation of recurrent neural network (RNN) that can control consecutive data due to the existence of update and reset gates. The gates decided on the necessary information to be kept in the memory. RNN-GRU is a simpler structure of long short-term memory (RNN-LSTM) with 87% of accuracy on prediction.

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