scholarly journals IDENTIFIKASI KOMODITAS UNGGULAN WILAYAH DALAM PERSPEKTIF PERTANIAN BERKELANJUTAN DI SULAWESI TENGGARA

2018 ◽  
Vol 7 (2) ◽  
pp. 92
Author(s):  
Zainal Abidin
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Agricultural Commodity ◽  
Location Quotient ◽  
Cashew Nut ◽  
Native Chicken ◽  
Long Time ◽  
Cocoa Plantation ◽  
Research Show

Developing of agricultural commodity at Southeast Sulawesi have specific ficture base on natural resources. The research was conducted to know the commodity that have sustainable competitifnes. Research was done using time series data 2010 – 2016 and analize with Location Quotien (LQ) dan Dinamic Location Quotient (DLQ) methode. The result of research show that food commoditeis that have become the sustaibnable bases commodity are corn, peanut and cassava, besides that rice and soybean only can became shor-term bases commodity. Livestock commodities such us sow, goat,  native chicken and rusting hens broiler also can became bases commodity for a long time in several regencies. Even that clove, cashew nut, coconut, pepper and cocoa plantation can became the bases of commodities in several regencies such us Buton, Muna, Konawe Selatan, Kolaka Utara, Konawe Utara, Kendari dan Bau-Bau. Horticulture commodities such us chili pepper, zingeber and orange also can became sustainable bases commodities. So developing that commoditeis should be have policy supporting such us kredit regulation, infrasctukture and also aplicatif and simple technologies.

Welfare Use in Japan: Trends and Determinants

2007 ◽  
pp. 88
Author(s):  
Wataru Suzuki ◽  
Yanfei Zhou
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Time Series Data ◽  
Positive Impact ◽  
Series Data ◽  
Negative Effects ◽  
Long Time ◽  
Welfare Use ◽  
The Impact

This article represents the first step in filling a large gap in knowledge concerning why Public Assistance (PA) use recently rose so fast in Japan. Specifically, we try to address this problem not only by performing a Blanchard and Quah decomposition on long-term monthly time series data (1960:04-2006:10), but also by estimating prefecturelevel longitudinal data. Two interesting findings emerge from the time series analysis. The first is that permanent shock imposes a continuously positive impact on the PA rate and is the main driving factor behind the recent increase in welfare use. The second finding is that the impact of temporary shock will last for a long time. The rate of the use of welfare is quite rigid because even if the PA rate rises due to temporary shocks, it takes about 8 or 9 years for it to regain its normal level. On the other hand, estimations of prefecture-level longitudinal data indicate that the Financial Capability Index (FCI) of the local government2 and minimum wage both impose negative effects on the PA rate. We also find that the rapid aging of Japan's population presents a permanent shock in practice, which makes it the most prominent contribution to surging welfare use.

News Article Based Industry Risk Index Prediction for Industry-Specific Evaluation

2021 ◽  
Author(s):  
Kyungwon Kim ◽  
Kyoungro Yoon
Keyword(s):  
Time Series ◽  
Time Delay ◽  
Evaluation Method ◽  
Time Series Data ◽  
Risk Index ◽  
News Article ◽  
Series Data ◽  
Specific Interest ◽  
Analysis Model ◽  
Long Time

The existing industry evaluation method utilizes the method of collecting the structured information such as the financial information of the companies included in the relevant industry and deriving the industrial evaluation index through the statistical analysis model. This method takes a long time to calculate the structured data and cause the time delay problem. In this paper, to solve this time delay problem, we derive monthly industry-specific interest and likability as a time series data type, which is a new industry evaluation indicator based on unstructured data. In addition, we propose a method to predict the industrial risk index, which is used as an important factor in industrial evaluation, based on derived industry-specific interest and likability time series data.

Cosinor Analysis for Temperature Time Series Data of Long Duration

2007 ◽  
Vol 9 (1) ◽  
pp. 30-41 ◽  
Author(s):  
Nikhil S. Padhye ◽  
Sandra K. Hanneman
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Model Fit ◽  
Series Data ◽  
Model Parameters ◽  
Cosinor Analysis ◽  
Long Time ◽  
Improved Model ◽  
Data Length ◽  
Cosinor Models

The application of cosinor models to long time series requires special attention. With increasing length of the time series, the presence of noise and drifts in rhythm parameters from cycle to cycle lead to rapid deterioration of cosinor models. The sensitivity of amplitude and model-fit to the data length is demonstrated for body temperature data from ambulatory menstrual cycling and menopausal women and from ambulatory male swine. It follows that amplitude comparisons between studies cannot be made independent of consideration of the data length. Cosinor analysis may be carried out on serial-sections of the series for improved model-fit and for tracking changes in rhythm parameters. Noise and drift reduction can also be achieved by folding the series onto a single cycle, which leads to substantial gains in the model-fit but lowers the amplitude. Central values of model parameters are negligibly changed by consideration of the autoregressive nature of residuals.

scholarly journals Evaluation of a new visualization and analytics solution for slow control data for large scale experiments

2020 ◽  
Vol 245 ◽  
pp. 07001
Author(s):  
Laura Sargsyan ◽  
Filipe Martins
Keyword(s):  
Time Series ◽  
Control System ◽  
Distribution System ◽  
Large Scale ◽  
High Energy Physics ◽  
Time Series Data ◽  
High Energy ◽  
Series Data ◽  
Test Version ◽  
Long Time

Large experiments in high energy physics require efficient and scalable monitoring solutions to digest data of the detector control system. Plotting multiple graphs in the slow control system and extracting historical data for long time periods are resource intensive tasks. The proposed solution leverages the new virtualization, data analytics and visualization technologies such as InfluxDB time-series database for faster access large scale data, Grafana to visualize time-series data and an OpenShift container platform to automate build, deployment, and management of application. The monitoring service runs separately from the control system thus reduces a workload on the control system computing resources. As an example, a test version of the new monitoring was applied to the ATLAS Tile Calorimeter using the CERN Cloud Process as a Service platform. Many dashboards in Grafana have been created to monitor and analyse behaviour of the High Voltage distribution system. They visualize not only values measured by the control system, but also run information and analytics data (difference, deviation, etc.). The new monitoring with a feature-rich visualization, filtering possibilities and analytics tools allows to extend detector control and monitoring capabilities and can help experts working on large scale experiments.

scholarly journals Kluster Tanaman Pangan Unggulan di Provinsi Jawa Tengah

2021 ◽  
Vol 21 (1) ◽  
pp. 55-68
Author(s):  
Choiroel Woestho ◽  
Milda Handayani ◽  
Adi Wibowo Noor Fikri
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Food Plants ◽  
Series Data ◽  
Food Crops ◽  
Food Crop ◽  
Land Area ◽  
Location Quotient ◽  
Central Java ◽  
Central Java Province

The food crop sector has an important role for regions in Indonesia. Food plants can be a determinant for an area in meeting the needs of the people in that area. In addition, the food crop sector, if developed, can become revenue for the region. This study aims to analyze the leading food plants in 35 districts / cities in Central Java Province. By using the location quotient (LQ) method and the Regional Specialization Index. The data used is time series data from 2014 to 2019 in 35 districts / cities in Central Java Province for food crops based on land area and production. The results obtained for the average LQ value of food crops based on land area, there are only 12 districts / cities which are the basis for superior food crops with Wonogiri Regency at the top. Meanwhile, based on the average LQ value based on production, only 11 districts / cities are the basis for superior food crops with Semarang Regency being the top. For the specialization index based on both land area and production, there is no Regency / City that specializes in Central Java Province.   Keywords: Foodcrop Sector, Location Quotient, Specialization Index, Central Java   Abstrak   Sektor tanaman pangan mempunyai peranan penting bagi daerah di Indonesia. Tanaman pangan dapat menjadi penentu bagi suatu daerah dalam memenuhi kebutuhan masyarakat yang ada di daerah tersebut. Selain itu, sektor tanaman pangan jika dikembangkan dapat menjadi pendapatan bagi daerah. Penelitian ini bertujuan untuk menganalisis tanaman pangan unggulan yang ada di 35 Kabupaten/Kota pada Provinsi Jawa Tengah. Dengan menggunakan metode location quotient (LQ) dan Indeks Spesialisasi Regional. Data yang digunakan adalah data time series selama tahun 2014 hingga tahun 2019 pada 35 Kabupaten/Kota di Provinsi Jawa Tengah untuk tanaman pangan berdasarkan luas lahan dan produksi. Hasil yang diperoleh untuk nilai rata – rata LQ tanaman pangan berdasarkan luas lahan, hanya terdapat 12 Kabupaten/Kota yang menjadi basis bagi tanaman pangan unggulan dengan Kabupaten Wonogiri berada di urutan teratas. Sementara berdasarkan nilai rata – rata LQ berdasarkan produksi, hanya 11 Kabupaten/Kota yang menjadi basis tanaman pangan unggulan dengan Kabupaten Semarang menjadi urutan teratas. Untuk indeks spesialisasi baik berdasarkan luas lahan dan produksi, tidak ada Kabupaten/Kota yang mempunyai spesialisasi terhadap Provinsi Jawa Tengah.   Kata kunci: Tanaman Pangan, Indeks Lokalisasi, Indeks Spesialisasi, Jawa Tengah

scholarly journals A Data-Driven Long Time-Series Electrical Line Trip Fault Prediction Method Using an Improved Stacked-Informer Network

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4466
Author(s):  
Li Guo ◽  
Runze Li ◽  
Bin Jiang
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Short Term Memory ◽  
Fault Prediction ◽  
Data Driven ◽  
Series Data ◽  
Sequence Prediction ◽  
Long Time Series ◽  
Long Time

The monitoring of electrical equipment and power grid systems is very essential and important for power transmission and distribution. It has great significances for predicting faults based on monitoring a long sequence in advance, so as to ensure the safe operation of the power system. Many studies such as recurrent neural network (RNN) and long short-term memory (LSTM) network have shown an outstanding ability in increasing the prediction accuracy. However, there still exist some limitations preventing those methods from predicting long time-series sequences in real-world applications. To address these issues, a data-driven method using an improved stacked-Informer network is proposed, and it is used for electrical line trip faults sequence prediction in this paper. This method constructs a stacked-Informer network to extract underlying features of long sequence time-series data well, and combines the gradient centralized (GC) technology with the optimizer to replace the previously used Adam optimizer in the original Informer network. It has a superior generalization ability and faster training efficiency. Data sequences used for the experimental validation are collected from the wind and solar hybrid substation located in Zhangjiakou city, China. The experimental results and concrete analysis prove that the presented method can improve fault sequence prediction accuracy and achieve fast training in real scenarios.

scholarly journals Observing the Ocean

Eos ◽  
2017 ◽  
Vol 98 ◽  
Author(s):  
Toste Tanhua
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Data Set ◽  
Long Time Series ◽  
Long Time

How measurements from a glider deployed off the coast of Peru are contributing to a much-needed long time-series data set.

scholarly journals ANALISIS PERTUMBUHAN DAN PERGESERAN SRUKTUR EKONOMI KABUPATEN INDUK DI PROVINSI BENGKULU

2020 ◽  
Vol 2 (1) ◽  
pp. 54-69
Author(s):  
Sunoto Sunoto ◽  
Bertha Iin Esti Indraswanti ◽  
Edy Rahmantyo Tarsilohadi
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Economic Development ◽  
Economic Performance ◽  
Time Series Data ◽  
Economic Structure ◽  
Series Data ◽  
Location Quotient ◽  
Dynamic Location ◽  
Secondary Time

The purpose of this research was to analyze economic growth and shifting of economic structure of the origin district in Bengkulu Province. Base on BPS secondary time series data (2001-2017), descriftive analysis was used to analyze economic growth and shifting economic structure, specialty after the region otonomous era (OTDA).  The DLQ and SSA method was used to determine the potential and leading sectors to increase economic performance. The result of this research was conclude that expansion of the the region in Bengkulu Provinsi has positif impact on economic development for the origin district. The economis structure was shifting from premier sector to secondary and tertier sector. The potential and leading sector after OTDA become more than before (from 4 or 5 sector to 7 untul 9 sector).  Keywords :  Dynamic Location Quotient 1, Shift Share Analysis 2, Economic Growth 3, Economic Structure 4, Potential and Leading Sector 5

scholarly journals IDENTIFICATION OF AGRICULTURAL SECTOR LEADING COMMODITIES IN MAGELANG DISTRICT

2020 ◽  
Vol 4 (2) ◽  
pp. 247-255
Author(s):  
Danta Paramartha ◽  
Mukson Mukson ◽  
Budi Adi Kristanto
Keyword(s):  
Agricultural Sector ◽  
Time Series Data ◽  
Secondary Data ◽  
Growth Patterns ◽  
Series Data ◽  
Agricultural Commodity ◽  
Sweet Potatoes ◽  
Location Quotient ◽  
Fast Growing ◽  
Central Java

Determination of leading commodities is needed to know the potential and prospective commodities that can be developed in an area. Leading commodities are expected to provide greater revenue compared to other commodities. This study aims to determine the leading agricultural subsector that has the potential to be developed as a driver of the economy in Magelang District. and analyze the trends in the growth patterns of subsector and leading agricultural commodities in the district. The method used in this study is descriptive method. The data used is time series data, namely secondary data from the GRDP of Magelang and Central Java Provinces in 2013-2017 and data on the amount of agricultural commodity production over a 7-year period (2011-2017). Data analysis tools used are Analysis of Location Quotient (LQ), Dynamic Location Quotient (DLQ), and Klassen Typology Analysis.The results of the study shows that, horticultural plants, livestock, forestry and natural resources are base / leading subsectors of agricultural sector in Magelang District. The pattern and structure of the growth of the food crop subsector is among those that are rapidly developing, while the horticulture and livestock are among the advanced but depressed subsectors and the most advanced and fast growing food crops are sweet potatoes. On the other hand, the fast-growing group of horticultural commodities of vegetables are cabbage, cayenne pepper, tomatoes, cauliflower, cucumber, scallion, and carrots, whereas horticultural commodities in advanced and fast-growing fruit groups are rambutan and salak. In addition, the rapidly growing livestock commodities are cows, buffaloes, pigs, native chickens, and ducks. Finally the advanced but depressed commodities are goats and broilers.

scholarly journals Analisis Potensi Ekonomi Pada Sektor Dan Subsektor Pertanian, Kehutanan Dan Perikanan Di Kabupaten Tambrauw Tahun 2013 -2017

2019 ◽  
Vol 2 (2) ◽  
pp. 17-25
Author(s):  
Sisilia Maria Parinusa
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Competitive Advantage ◽  
Time Series Data ◽  
Research Result ◽  
Value Added ◽  
Series Data ◽  
Gdp Growth ◽  
Reference Area ◽  
Location Quotient

Agriculture, forestry and fishery sector has an important role in generating the economic growth in Tambrauw Regency. It can be seen from the amount of its contribution to the gross value added which is more that 33 percent. The aim of this study is to identify and analyse the potential subsector of agriculture, forestry and fisheries sector in Tambrauw Regency. By using Location Quotient (LQ) Method and Shift Share analysis and supported by a time series data of GDP growth between the study area and reference area in the recent five years the potential subsectors can be determined. The research result reveals that food plant, horticulture group and forestry and logging subsector are the potential subsectors to promote the regional income due to their location quotient greater than 1 and have positive competitive advantage values.

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