Aplication of ARIMA Model for Forecasting Additional Positive Cases of Covid-19 in Jember Regency

Hariadi, Wigid; Sulantari, Sulantari

doi:10.20885/enthusiastic.vol1.iss1.art4

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Even though it is in string format, it is not an issue as it is set as an index. The transformation begins with the calculation data value of the p-value using the Augmented Dickey-Fuller (ADF) test [21]. ADF test was performed for the time series used for the study on the unit roots.…”

Section: Data Preparationmentioning

confidence: 99%

Autoregressive Integrated Moving Average (ARIMA) Algorithm Adaptation for Business Financial Forecasting

Khyrina Airin Fariza Abu Samah,

Nurul Azifah Mohd Khalid,

Jamaluddin Jasmis

et al. 2024

ARASET

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Financial management is the key to running a successful business, and financial forecasting is crucial to every business. Companies face difficulties making the right decision regarding their goals as they experience uncertainties in ensuring business growth. This study uses Dutch Lady company as a case study to help identify the company’s performance based on eight financial ratios and visualize the results using the visualization technique. Hence, the autoregressive integrated moving average (ARIMA) algorithm and visualization techniques were applied to overcome the problems. The data is obtained from the last quarterly report for the study purpose. There are 14 variables collected and used to calculate the eight financial ratios. The predictive modelling uses the ARIMA algorithm, and the models were evaluated using two types of error metrics: 1) mean absolute error (MAE) and 2) root mean squared error (RMSE). The reliability testing for the system’s prediction model shows a result p-value<0.05, and functionality testing successfully met the aims. The error metric evaluation result shows no significant differences between the forecast and actual values. The models can appropriately predict and forecast the financial ratio as the rules for both MAE and RMSE are fulfilled. The forecast results of each financial ratio are visualized and presented through the web-based system.

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Section: Data Preparationmentioning

confidence: 99%

Autoregressive Integrated Moving Average (ARIMA) Algorithm Adaptation for Business Financial Forecasting

Khyrina Airin Fariza Abu Samah,

Nurul Azifah Mohd Khalid,

Jamaluddin Jasmis

et al. 2024

ARASET

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“…Andini dan Utomo [8] juga memprediksi data iklim kota Bandung Jawa Barat dengan menggunakan kombinasi RNN dan Long Short Term Memory (LSTM). Prediksi cuaca ekstrim dengan pendekatan klasik juga telah dilakukan oleh beberapa ahli seperti model Autoregressive Integrated Moving Average (ARIMA) telah dilakukan [9][10][11]. Beberapa hasil riset yang telah disebutkan menunjukkan bahwa model ARIMA sangat baik digunakan pada data deret waktu dengan periode pendek.…”

Section: Pendahuluanunclassified

Peramalan Data Cuaca Ekstrim Indonesia Menggunakan Model ARIMA dan Recurrent Neural Network

Hikmah

Asrirawan²,

Apriyanto³

et al. 2023

Jambura J. Math

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Extreme weather modeling is a challenge for modeling experts in Indonesia and the world. Extreme weather prediction is a complex problem because the chances of it happening are very small, so the developed models often have a low level of accuracy. The purpose of this research is to combine the classic model, Autoregressive Integrated Moving Average (ARIMA), recurrent neural network (RNN) model using Adam and SGD estimation (RNN-Adam and RNN-SGD) with the reLU, tanh, sigmoid and gaussian activation functions. In addition, the ARIMA-RNN mix model was also demonstrated in this study. These models are applied to monthly period extreme weather data obtained from the Meteorology, Climatology and Geophysics Agency (BMKG) of West Sulawesi Province which are converted into training data and test data. The RMSE value is used to see the level of prediction accuracy in both training data and test data. Based on the research results, the best model obtained for modeling Indonesia’s extreme weather is the ARIMA-RNN-Adam mix model with the reLU activation function based on the RMSE value on the training and test data. At n = 50, the smallest RMSE and MSE values of the third model are the ARIMA-RNN-Adam model which is 0.23212 using the reLU activation function, then the ARIMA-RNN-SGD model which is 0.25432 with the same activation function, while the ARIMA value is 0.3270. At n=100 it can be seen that the smallest RMSE and MSE values of the three models are the ARIMA-RNN-Adam model which is equal to 0.25149 using the reLU activation function, then the ARIMA-RNN-SGD model which is equal to 0.25256 with the same activation function, while the ARIMA value is 0.2644.

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“…The Autoregressive Integrated Moving Average (ARIMA) model is popular for forecasting univariate time series datasets. This method consists of four main stages: identification, parameter assessment, diagnostic examination, and forecasting using the ARIMA model (p, d, q) (Hariadi & Sulantari, 2021). ARIMA forecasting equation for stationary time series is a linear equation like a regression in which Open Access the predictors are composed of the dependent variable lag and the estimated lag error.…”

Section: Autoregressive Integrated Moving Average Model (Arima Model)mentioning

confidence: 99%

The Prospects of Lampung's Pepper Export to the Global Market: An Analysis Using the ARIMA Model

Putri,

Abidin,

Situmorang

2023

HBT

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Pepper is one of Lampung's leading export commodities. It can be seen from the contribution of Lampung Province's pepper, which accounted for 42 percent of Indonesia's overall pepper exports. However, pepper production and export volume in Lampung Province continue to decline annually. This study aims to analyze the prospect of Lampung’s pepper export to the international market for ten years, from 2023 - 2033. This research used ARIMA (Auto Regressive Moving Average) t model tool using E-views statistical software to forecast the trend of export of Lampung pepper to the International market. The data used was secondary data from the quarterly export of Lampung’s pepper from 2002 to 2022. The study suggested that Lampung's pepper exports are projected to decrease from 2023 to 2033, with a decrease of 10 percent each year. Finally, in 2033, Lampung's pepper exports to the international market only reached 998 tons.

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Aplication of ARIMA Model for Forecasting Additional Positive Cases of Covid-19 in Jember Regency

Cited by 3 publications

References 4 publications

Autoregressive Integrated Moving Average (ARIMA) Algorithm Adaptation for Business Financial Forecasting

Autoregressive Integrated Moving Average (ARIMA) Algorithm Adaptation for Business Financial Forecasting

Peramalan Data Cuaca Ekstrim Indonesia Menggunakan Model ARIMA dan Recurrent Neural Network

The Prospects of Lampung's Pepper Export to the Global Market: An Analysis Using the ARIMA Model

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