Generalized Space Time Autoregressive of Chili Prices

Jamilatuzzahro,; Caraka, Rezzy Eko; Herliansyah, Riki; S, Asmawati; Sari, Dian Megah; Pardamean, Bens

doi:10.1109/icimtech.2018.8528117

Cited by 10 publications

(6 citation statements)

References 5 publications

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“…. , w (L) ) T (7) Equation ( 6) states that w (l) 10 is the weight in the lth hidden layer, the data input-1, and the neuron-0. Moreover, w (l) 11 is the weight on the hidden layer to-l, input data to-1 and neuron-1, continuing until weight-l, hidden layer-ml, and neuron(ml-1).…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

“…Furthermore, the influence of rainfall, as a climate factor, can be estimated using GSTAR to plan rice planting seasons, which vary across different sites. GSTARIMA is employed to analyze the distribution of yields and determine pricing based on the transfer function [7][8][9]. These applications extend beyond agriculture, encompassing various sectors [10].…”

Section: Introductionmentioning

confidence: 99%

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Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting

et al. 2023

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The issue of climate change holds immense significance, affecting various aspects of life, including the environment, the interaction between soil conditions and the atmosphere, and agriculture. Over the past few decades, a range of spatio-temporal and Deep Neural Network (DNN) techniques had been proposed within the field of Machine Learning (ML) for climate forecasting, using spatial and temporal data. The forecasting model in this paper is highly complex, particularly due to the presence of nonlinear data in the residual modeling of General Space-Time Autoregressive Integrated Moving Average (GSTARIMA), which represented nonstationary data with time and location dependencies. This model effectively captured trends and seasonal data with time and location dependencies. On the other hand, DNNs proved reliable for modeling nonlinear data that posed challenges for spatio-temporal approaches. This research presented a comprehensive overview of the integrated approach between the GSTARIMA model and DNNs, following the six-stage Data Analytics Lifecycle methodology. The focus was primarily on previous works conducted between 2013 and 2022. The review showed that the GSTARIMA–DNN integration model was a promising tool for forecasting climate in a specific region in the future. Although spatio-temporal and DNN approaches have been widely employed for predicting the climate and its impact on human life due to their computational efficiency and ability to handle complex problems, the proposed method is expected to be universally accepted for integrating these models, which encompass location and time dependencies. Furthermore, it was found that the GSTARIMA–DNN method, incorporating multivariate variables, locations, and multiple hidden layers, was suitable for short-term climate forecasting. Finally, this paper presented several future directions and recommendations for further research.

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Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting

et al. 2023

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“…The information technology infrastructure for genotyping studies in agriculture [26][27][28] must provide fast processors, a flexible and scalable data storage and backup system, a large memory to process data, and methods to retrieve relevant variables [29]. The infrastructure must also provide sufficient bandwidth to transfer files between different sites [30] CropX provides optimal irrigation mapping and planning for farmers, which includes soil sensors that measure soil moisture and temperature and upload data to the cloud [31].…”

Section: Related Workmentioning

confidence: 99%

Smart IoT CPO Storage Tank

Renjani

Hermantoro

Nugraha

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Indonesia’s crude palm oil (CPO) production from year to year continues to increase, at the end of 2020 it reached 17.35 million tons, up 3.6% from the previous year. Increasing production will result in more CPO stock and require good storage. The storage process that occurs is to maintain the temperature of the CPO so that the quality is not damaged. This temperature regulation is still done manually and raises the risk of work accidents. The purpose of this research is to create a temperature control system and automatic volume measurement that can be monitored from a smartphone. The manufacture of a control system used ESP8266 NodeMCu microcontroller, temperature sensor, proximity sensor, and 1000-Watt heater. Programming used the Arduino IDE and C++. The result of this study was an IoT CPO Storage Tank design equipped with sensors and microcontrollers. The temperature was measured with the DS18B20 sensor had a data accuracy of 99.19% while the volume measured with the HC-SR04 sensor had an accuracy of 99.78%. Data obtained from the sensor could be seen through the Thingspeak application from a computer or smartphone.

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“…Generalized Linear Model (GLM) adalah bentuk perluasan dari regresi linier sederhana dengan asumsi distribusi variabel respon merupakan anggota dari keluarga eksponensial. Menurut [2], tiga komponen penting yang membentuk Generalized Linear Model adalah, variabel prediktor yang linier, distribusi dari variabel respon adalah distribusi keluarga eksponensial, dan ada fungsi link (fungsi penghubung) yaitu fungsi yang menjelaskan nilai ekspektasi dari variabel respon dengan menghubungkan variabel penjelas melalui persamaan linier.…”

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Pendekatan Generalized Linear Model Pada Regresi Kuantil Copula Normal Untuk Keterhubungan IHSG dan Kurs EUR-IDR

Prameswara¹,

Susanto²,

Sasongko³

2020

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Penelitian ini bertujuan untuk memperoleh estimasi parameter dan regresi kuantil pada suatu model distribusi bivariat yang disebut Copula sebagai alternatif regresi linier klasik dalam menganalisis keterhubungan dua peubah acak. Copula adalah model distribusi bivariat yang memiliki keunggulan selain karena tidak kaku terhadap asumsi distribusi tertentu, juga dapat menyatakan keterhubungan nonlinier. Copula yang dianalisis pada penelitian ini adalah Copula Normal. Sedangkan Generalized Linear Model (GLM) adalah perluasan dari model regresi linier klasik, yang salah satu komponen utamanya adalah fungsi link. Didapati bahwa regresi kuantil pada copula Normal merupakan suatu bentuk GLM dengan fungsi invers link yaitu . Regresi kuantil dan parameter copula Normal diestimasi dengan pendekatan GLM menggunakan metode Least Square. Estimasi regresi kuantil terbaik dilakukan dengan menghitung Mean Square Error (MSE). Validasi parameter copula dilakukan melalui simulasi dengan parametric bootstrap. Data yang digunakan dalam penelitian ini adalah data return IHSG sebagai peubah tak bebas dan data return kurs beli EUR-IDR sebagai peubah bebas. Hasil penelitian menunjukkan bahwa keterhubungan IHSG dan kurs beli EUR-IDR lemah dan tidak linier.

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Generalized Space Time Autoregressive of Chili Prices

Cited by 10 publications

References 5 publications

Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting

Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting

Smart IoT CPO Storage Tank

Pendekatan Generalized Linear Model Pada Regresi Kuantil Copula Normal Untuk Keterhubungan IHSG dan Kurs EUR-IDR

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