Identification of Extreme Rainfall Pattern Using Extremogram in West Java

Rinaldi, Achi; Djuraidah, Anik; Wigena, Aji Hamim; Mangku, I Wayan; Gunawan, Dodo

doi:10.1088/1755-1315/187/1/012064

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…Various aspects of extremograms were later discussed, including the asymptotic theory (Davis et al, 2013;Cho et al, 2016;Matsui and Mikosch, 2016) and permutation methods to construct confidence bands for extremograms (Davis et al, 2012;Drees, 2015). It has been recently applied to measure serial dependence of extreme observations and co-movements in stock markets (Bollerslev et al, 2013;Herrera and Clements, 2018;Hautsch and Herrera, 2020), credit default swap (CDS) markets (Cont and Kan, 2011) and extreme rainfall pattern identification (Rinaldi et al, 2018). Furthermore, apart from using extremograms to measure the extremal dependence, Mikosch and Zhao (2015) implement a goodness of fit test in the frequency domain to conduct model selection based on the ability of extremograms to explain the dependence in tails.…”

Section: Related Literaturementioning

confidence: 99%

Extremal Dependence in Australian Electricity Markets

Lin¹,

Cribben²,

Trueck³

2022

Preprint

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Electricity markets are significantly more volatile than other comparable financial or commodity markets. Extreme price outcomes and their transmission between regions pose significant risks for market participants. We examine the dependence between extreme spot price outcomes in the Australian National Electricity Market (NEM). We investigate extremal dependence both in a univariate and multivariate setting, applying the extremogram developed by Davis and Mikosch (2009) and Davis et al. (2011Davis et al. ( , 2012. We measure the persistence of extreme prices within individual regional markets and the transmission of extreme prices across different regions. With both 5-minute and 30minute price data, we find that extreme prices are more persistent in the market with a higher share of intermittent renewable energy. We also find that persistence of extreme prices is more prevalent in more concentrated markets. We also show significant extremal price dependence between different regions, which is typically stronger between physically interconnected markets. The dependence structure of extreme prices shows asymmetric and time-dependent patterns. Applying the extremograms, we further show the effectiveness of the Australian Energy Market Commission's 2016 rebidding rule with respect to reducing the share of isolated price spikes that are often considered as an indication of strategic bidding. Our results provide important information for hedging decisions of market participants and for policy makers who aim to reduce market volatility and extreme price outcomes through effective regulations which guide the trading behaviour of market participants as well as improved network interconnections.

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Section: Related Literaturementioning

confidence: 99%

Extremal Dependence in Australian Electricity Markets

Lin¹,

Cribben²,

Trueck³

2022

Preprint

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“…Identifikasi variabel merupakan hal yang sangat krusial untuk menentukan langkah pemodelan ke depannya (Rinaldi et al, 2018) Dalam lingkup rumah tangga, kesejahteraan suatu rumah tangga umumnya dipengaruhi oleh kondisi internal rumah tangga, meliputi pendidikan, pekerjaan (ketenagakerjaan), ekonomi, jumlah anggota rumah tangga, dan lain-lain yang dapat di jelaskan secara matematis dampak dan pengaruhnya (Darmani & Renaldi, 2018). Namun dari faktor-faktor yang mempengaruhi kesejahteraan tersebut, faktor pendidikan dan pekerjaan dinilai memiliki pengaruh yang cukup menentukan terhadap kesejahteraan.…”

Section: Pendahuluanunclassified

Model Persamaan Struktural untuk Menganalisis Indikator Kesejahteraan Rumah Tangga

Rinaldi

2019

DJM

Self Cite

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AbstrakKesejahteraan merupakan hal penting yang menjadi perhatian semua negara di dunia, termasuk Indonesia. Kesejahteraan tidak hanya diukur secara material tetapi juga diukur secara rohani. Secara material, kesejahteraan diukur dari kekayaan yang dimiliki seseorang, kesehatan, gizi, pendidikan, aset, perumahan, dan hak-hak tertentu dalam masyarakat. Sedangkan secara rohani kesejahteraan diukur dari kebahagiaan yang dirasakan. Penelitian ini membahas penerapan model persamaan struktural untuk mengkaji keterkaitan antar indikator kesejahteraan di Provinsi Jawa Tengah. Model yang dibangun menempatkan pendidikan dan pekerjaan sebagai peubah laten eksogen, sedangkan kesejahteraan obyektif dan kesejahteraan subyektif sebagai peubah laten endogen. Data yang digunakan untuk mendukung analisis bersumber dari BPS, hasil Survei Sosial Ekonomi Nasional KOR dan MSBP 2012, dengan ukuran sampel 6730 pengamatan. Hasil pemodelan didapat model yang cukup layak untuk menjelaskan keragaman data, yang ditunjukkan oleh nilai GFI 0.97, AGFI 0.96, RMSEA 0.039, dan RMSR 0.072. Hasil analisis menunjukkan pendidikan dan pekerjaan memiliki pengaruh langsung terhadap kesejahteraan obyektif dan pengaruh tak langsung terhadap kesejahteraan subyektif. Pengaruh pendidikan terhadap tingkat kesejahteraan lebih tinggi dibandingkan pengaruh pekerjaan.Kata kunci: peubah laten, indikator, maximum likelihood, GFI, AGFI, RMSEA, RMSR. AbstractWelfare is an important thing that concerns all countries in the world, including Indonesia. Welfare is not only measured materially but also spiritually measured. Materially, welfare is measured by one 's wealth, health, nutrition, education, assets, housing, and certain rights in society. While spiritually well-being is measured by perceived happiness. This study discusses the application of the structural equation model to examine the relationship between welfare indicators in Central Java Province. The model-built places education and employment as exogenous latent variables, while objective well-being and subjective well-being are endogenous latent variables. The data used to support the analysis were sourced from BPS, the results of the 2012 KOR National Social Economic Survey and MSBP, with a sample size of 6730 observations. The modeling results obtained by the model are quite feasible to explain the diversity of data, which is indicated by the value of GFI 0.97, AGFI 0.96, RMSEA 0.039, and RMSR 0.072. The analysis shows that education and employment have a direct influence on objective well-being and an indirect effect on subjective well-being. The effect of education on the level of welfare is higher than the effect of work.

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“…Pattern recognition is a ubiquitous technique, usually carried out to automatically recognise similarities and regularities among datasets, which has recently been applied in hydroclimatic sciences [1][2][3][4]. The general approach in these applications follows a series of steps of segmenting hydroclimatic variables or phenomena into sections, selecting features and then classifying patterns [5].…”

Section: Introductionmentioning

confidence: 99%

A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets

Han

Xuan

2022

Remote Sensing

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This paper presents the development and applications of a new, open-source toolbox that aims to provide automatic identification and classification of hydroclimatic patterns by their spatial features, i.e., location, size, orientation, and shape, as well as the physical features, i.e., the areal average, total volume, and spatial distribution. The highlights of this toolbox are: (1) incorporating an efficient algorithm for automatically identifying and classifying the spatial features that are linked to hydroclimatic extremes; (2) use as a frontend for supporting AI-based training in tracking and forecasting extremes; and (3) direct support for short-term nowcasting of extreme rainfall via tracking rainstorm centres and movement. The key design and implementation of the toolbox are discussed alongside three case studies demonstrating the application of the toolbox and its potential in helping build machine learning applications in hydroclimatic sciences. Finally, the availability of the toolbox and its source code is included.

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Identification of Extreme Rainfall Pattern Using Extremogram in West Java

Cited by 7 publications

References 6 publications

Extremal Dependence in Australian Electricity Markets

Extremal Dependence in Australian Electricity Markets

Model Persamaan Struktural untuk Menganalisis Indikator Kesejahteraan Rumah Tangga

A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets

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