scholarly journals Growth and Instability Analysis of Groundnut Production in India and Karnataka

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Akshata Nayak
Keyword(s):  
Time Series Data ◽  
Cultural Practice ◽  
Series Data ◽  
Policy Makers ◽  
Productivity Level ◽  
Predominant Component ◽  
Average Production ◽  
Change In The Mean ◽  
The Mean ◽  
The Tropics

Groundnut is grown throughout the tropics and extended to the subtropical countries. India is the second largest producer of groundnut in the world after China. The fact that groundnut crops in India, particularly in Karnataka are mainly covered under rain fed situation, which in turn has to depend on the arrival of monsoon, climatic changes and drought. Hence, the productivity level of groundnut crop was erratic. It was in this backdrop, an attempt was made through the present study to examine the growth and instability of groundnut in India and Karnataka by way of analyzing the time series data of 48 years. The results revealed that the level of instability was marginally higher in groundnut area (8.7 %) during period II compared to period I (2.9%) and period III (7.3%). The variation in production and yield of groundnut was higher during the period III compared to period I and II. Change in the mean area is contributing more to change in average production of groundnut in India and in Karnataka followed by interaction between changes in mean area and mean yield. Change in area variance is the predominant component contributing to the change in variance of production of groundnut in India as well as in Karnataka. From the outcome of the result, it is concluded that the researchers and policy makers have to take more attention to develop location specific cultural practice to increase and sustain groundnut production and yield in the nation.

DOI: https://doi.org/10.36873/ae , Frits Fahridws Damanik

2020 ◽  
Vol 13 (02) ◽  
pp. 1-8
Author(s):  
Agrienvi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Arima Model ◽  
Series Data ◽  
Production Data ◽  
Policy Makers ◽  
Strategic Value ◽  
Modeling And Forecasting ◽  
The Mean ◽  
Arima Modeling

ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean so thatmust differenced while the data on the production of small chilli carried out the stages of data transformation anddifferencing due to the unstationer of data on variants and the mean. The best ARIMA model that can be applied basedon the smallest AIC and MSE criteria for data on the amount of chili and small chilli production in Central KalimantanProvince is ARIMA (3,1,0).Keywords: modeling of chilli, forecasting of chilli, Autoregresive Integrated Moving Avarage, ARIMA, Box-Jenkins.

Frits Fahridws Damanik

2020 ◽  
Vol 13 (02) ◽  
pp. 1-8
Author(s):  
Agrienvi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Arima Model ◽  
Series Data ◽  
Production Data ◽  
Policy Makers ◽  
Strategic Value ◽  
Modeling And Forecasting ◽  
The Mean ◽  
Arima Modeling

ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean sothat must differenced while the data on the production of small chilli carried out the stages of data transformation anddifferencing due to the unstationer of data on variants and the mean. The best ARIMA model that can be appliedbased on the smallest AIC and MSE criteria for data on the amount of chili and small chilli production in CentralKalimantan Province is ARIMA (3,1,0).Keywords: modeling of chilli, forecasting of chilli, Autoregresive Integrated Moving Avarage, ARIMA, Box-Jenkins.

scholarly journals Distance variable improvement of time-series big data stream evaluation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ari Wibisono ◽  
Petrus Mursanto ◽  
Jihan Adibah ◽  
Wendy D. W. T. Bayu ◽  
May Iffah Rizki ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Standard Method ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Traffic Demand ◽  
Incremental Model ◽  
Chernoff Bound ◽  
The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

scholarly journals The impact of bank-specific and macroeconomic variables on profitability of Islamic rural bank in Indonesia

2019 ◽  
Vol 5 (3) ◽  
Author(s):  
Muhammad Sanusi
Keyword(s):  
Banking Sector ◽  
Time Series Data ◽  
Series Data ◽  
Estimation Model ◽  
Policy Makers ◽  
Vector Error Correction ◽  
Macroeconomic Variables ◽  
Vector Error ◽  
The Impact

This paper investigates the impact of bank-specific and macroeconomic variables on the profitability of Islamic rural bank (BPRS) in Indonesia. Using monthly time series data from January 2010 - December 2018. The estimation model used is a vector error correction model to analyze the long-term and short-term relationships between bank-specific and macroeconomic variables on the profitability of Islamic rural bank. The results showed that CAR and LnTA had a significant positive relationship, while NPF, BOPO and IPI had a negative and significant relationship to the profitability of Islamic rural banks. But FDR and Inflation variables are not significantly related to the profitability of Islamic rural bank. The results leave implications for policy makers, investors and banking sector managers. Based on evidence that bank profitability is more influenced by internal banks (as specific as banks), this research can help Islamic rural banks to help them understand which factors are important to be analyzed to obtain higher profitability.

A New Imputation Method for Treating Missing Precipitation Records

2014 ◽  
Vol 635-637 ◽  
pp. 1488-1495
Author(s):  
Yu Liu ◽  
Feng Rui Chen
Keyword(s):  
Time Series Data ◽  
Absolute Error ◽  
Imputation Method ◽  
Temporal Information ◽  
Series Data ◽  
Precipitation Trend ◽  
Monthly Total Precipitation ◽  
The Mean ◽  
Spatio Temporal ◽  
Precipitation Records

This study aims to present a new imputation method for missing precipitation records by fusing its spatio-temporal information. On the basis of extending simple kriging model, a nonstationary kriging method which assumes that the mean or trend is known and varies in whole study area was proposed. It obtains precipitation trend of each station at a given time by analyzing its time series data, and then performs geostatistical analysis on the residual between the trend and measured values. Finally, these spatio-temporal information is integrated into a unified imputation model. This method was illustrated using monthly total precipitation data from 671 meteorological stations of China in April, spanning the period of 2001-2010. Four different methods, including moving average, mean ratio, expectation maximization and ordinary kriging were introduced to compare with. The results show that: Among these methods, the mean absolute error, mean relative error and root mean square error of the proposed method are the smallest, so it produces the best imputation result. That is because: (1) It fully takes into account the spatio-temporal information of precipitation. (2) It assumes that the mean varies in whole study area, which is more in line with the actual situation for rainfall.

scholarly journals Penerapan Ukuran Ketepatan Nilai Ramalan Data Deret Waktu dalam Seleksi Model Peramalan Volume Penjualan PT Satriamandiri Citramulia

2011 ◽  
Vol 2 (2) ◽  
pp. 636 ◽  
Author(s):  
Iwa Sungkawa ◽  
Ries Tri Megasari
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Exponential Smoothing ◽  
Series Data ◽  
Percentage Error ◽  
Forecasting Model ◽  
Data Series ◽  
Past Data ◽  
The Mean

Forecasting is performed due to the complexity and uncertainty faced by a decision maker. This article discusses the selection of an appropriate forecasting model with time series data available. An appropriate forecasting model is required to estimate systematically about what is most likely to occur in the future based on past data series, so that errors (the differences between what actually happens and the results of the estimation) can be minimized. A gauge is required to detect the required the value of forecast accuracy. In this paper ways of forecasting accuracy of detection are discussed using the mean square error (MSE) and the mean absolute percentage error (MAPE). The forecasting method uses Moving Average, Exponential Smoothing, and Winters method. With the three methods forecast value is determined and the smallest value of MSE and Mape is selected. The results of data analysis showed that the Exponential Smoothing is considered an appropriate method to forecast the sales volume of PT Satriamandiri Citramulia because it produces the smallest value of MSE and Mape. 

scholarly journals Study of Maximum Sustainable Yield of Rabbitfish (Siganus sp.) in Makassar Waters

2020 ◽  
pp. 98-107
Author(s):  
Moh. Tauhid Umar ◽  
Sharifuddin Bin Andy Omar ◽  
Suwarni Suwarni
Keyword(s):  
Time Series Data ◽  
Production Method ◽  
Maximum Sustainable Yield ◽  
Series Data ◽  
Annual Data ◽  
Sustainable Yield ◽  
Catch Per Unit Effort ◽  
Total Allowable Catch ◽  
Average Production ◽  
Fisheries Statistics

This study aims to estimate the potential of fish resources which include catch per unit effort (CPUE), Maximum Sustainable Yield (MSY), optimum efforts, exploitation rates and total allowable catch (TAC) of Rabbitfish in Makassar waters. This study uses time-series data, namely annual data from the fisheries statistics report at the Office of Marine and Fisheries Service, South Sulawesi Province from 2007 until. 2016. The method used to predict fish resource potential in the study is the Surplus Production method referring to the Schaefer model. The results of the study showed that the average production and standard efforts in the 10-year period were 78.8 tons per year and 1304 standard units per year respectively. The estimation results obtained by MSY and the optimum efforts of rabbitfish per year were 104 tons and 1142 standard units, respectively and total allowable catches (TAC) are 82.979 tons per year. The level of utilization of rabbitfish resources in Makassar waters in the last three years has been overexploitation.

scholarly journals Financial Inclusion and the Trade-Growth Nexus: Evidence from the Emerging Zimbabwean Economy

2019 ◽  
Vol 10 (6A) ◽  
pp. 43-55 ◽  
Author(s):  
Alexander Maune
Keyword(s):  
Time Series Data ◽  
Trade Openness ◽  
The Body ◽  
Financial Inclusion ◽  
Series Data ◽  
Complementarity Effect ◽  
Policy Makers ◽  
Academic Knowledge ◽  
Body Of Knowledge ◽  
Trade Growth

This study examined the effect of financial inclusion in the trade-growth nexus in Zimbabwe using time series data collected from the World Bank databases from 1980 to 2016. The study precisely examined whether financial inclusion is a passage within which trade openness impacts growth in Zimbabwe. Also examined was the complementarity effect of financial inclusion and trade openness on growth. The effect of financial inclusion and trade openness on growth has received much attention from researchers across the globe and literature is awash with theoretical and empirical evidence of such studies. What is yet to be examined is whether financial inclusion is a passage within which trade openness influences growth. The study finds a negative significant effect of financial inclusion and trade openness on growth in Zimbabwe. Moreover, the findings show a complementary, strong and positive nexus linking financial inclusion and trade openness on growth in Zimbabwe. Policy-makers are, however, implored to formulate policies meant to deepen financial inclusion in order to enhance growth through trade openness. The article will help expand the academic knowledge and as such contribute in filling the gap that exists within the body of knowledge. The article is important to policy-makers, the academia, private sector and researchers at large.

scholarly journals The Role of Cooperatives, Remittances, and Infrastructure in Export Performance of Nepal: ARDL Approach of Cointegration

2020 ◽  
pp. 151-159
Author(s):  
Ramesh C. Paudel ◽  
Chakrapani Acharya ◽  
Resham Thapa-Parajuli
Keyword(s):  
Time Series Data ◽  
Export Performance ◽  
Series Data ◽  
Supply Side ◽  
Policy Makers ◽  
Ardl Approach ◽  
Negative Role ◽  
Supply Side Factors

Cooperatives, remittances, and foreign direct investment (FDI) are crucial source of funds required for better entrepreneurships, which combinedly along with the quality of infrastructure can contribute to enhance the supply side factors of the export performance. Due to the well perceived role of cooperatives, Nepal’s constitution 2015 mentions this sector as one of the three pillars of the national economy while around 30 percent of Nepal’s GDP comes from remittances. As the country lacks the domestic sources for investment, FDI has become an indispensable part of the development sources of the developing countries in the recent decades. This paper analyzes the role of cooperatives, remittances, FDI and infrastructure in export performance of Nepal using the Autoregressive Distributive Lag (ARDL) approach of cointegration as suggested by the properties of the time series data for the period of 26 years from 1993 to 2018. The major finding shows that the cooperatives have not contributed to export performance as expected, however the role is positive. The remittances have a strong negative role on export performance, which is largely impacted by the number and quality of the infrastructure. The role of FDI is also negative and might be due to insufficient volume to contribute substantially. This fact seeks the urgent attention from the policy makers to make the country more investment friendly.

scholarly journals Decomposition of Economic Growth in Sri Lanka: Deep Look into the Service Sector

2018 ◽  
Vol 10 (3) ◽  
pp. 1416-1422
Author(s):  
Pivithuru Janak Kumarasinghe ◽  
M P M D Sandaruwan
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Sri Lanka ◽  
Service Sector ◽  
Time Series Data ◽  
Series Data ◽  
Specific Service ◽  
Policy Makers ◽  
Research Findings ◽  
Per Capita

The service sector gives the highest contribution to the economic growth of the country and it is about more than 50. Therefore service sector give the highest contribution for the economic growth in Srilanka. Through this research the service sector is decomposed. This empirical study was to measuring the contribution for the economic growth in Sri Lanka by service sector. Time series data is used to identify the decomposition of economic growth in Sri Lanka by Service. Annually data is collected from 2006 to 2014. This study mainly focused on growth decomposition methodology developed by Ivanov and Webster and this methodology used to decompose economic growth in Sri Lanka by service sector. This model presents an approach that is general and it can be applied to other countries. The methodology identifies the direct impacts of specific service sector components on the per capita growth of real gross domestic product. The study found that each service sector components in this analysis has a very different contribution to the growth rate in the economy. The research findings would provide guidance to the policy makers to develop policies, procedures, programs and standards.

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