Application Seasonal Autoregressive Integrated Moving Average to Forecast the Number of East Kalimantan Hotspots

Wahyuningsih, Sri; Goejantoro, Rito; Siringoringo, Meiliyani; Saputra, A. R.; Aminah, Siti

doi:10.1088/1742-6596/1351/1/012085

Cited by 2 publications

(1 citation statement)

References 1 publication

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“…The data is stationary in variance by looking at the Box-Cox Transformation (Wahyuningsih, Goejantoro, Siringoringo, Saputra, & Aminah, 2019). Data is said to be stationary in variance if the value of lambda (λ) or the rounded value in the Box-Cox plot is 1 or more than 1 (Hyndman et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Forecasting prevalence of dengue hemorrhagic fever using ARIMA model in Sulawesi Tenggara Province, Indonesia

Mistawati

Yasnani

Lestari

2021

PHI

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Background: Dengue hemorrhagic fever occurs through the bite of Aedes mosquitoes, primarily Aedes aegypti, carrying dengue viruses. In recent decades, the risk increased dramatically, not only in the tropics but also in subtropical regions.Objective: This study aimed to determine the best model for forecasting dengue hemorrhagic fever prevalence in Sulawesi Tenggara, Indonesia.Method: This was a retrospective analytical study using secondary data from the Sulawesi Tenggara Provincial Health Office from 2014 to 2019. ARIMA model was used for data analysis.Results: ARIMA (0.1.1)(0.1.1)4 was selected as the best-suited model. Based on the forecast, there would be an increase in dengue hemorrhagic fever prevalence over the next two years, with a mean absolute percentage error value of 4.41%.Conclusion: Forecasting results indicated that the peaks of dengue hemorrhagic fever cases would be in March, July, and November, and the increase will occur in the same months each year. Also, forecasting results were very good. Public health practitioners can use this model to prevent and eradicate dengue hemorrhagic fever. The ARIMA model would also be useful for nursing practice in caring for patients with dengue fever in the future.

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Section: Discussionmentioning

confidence: 99%

Forecasting prevalence of dengue hemorrhagic fever using ARIMA model in Sulawesi Tenggara Province, Indonesia

Mistawati

Yasnani

Lestari

2021

PHI

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Implementation of Long Short-Term Memory for Gold Prices Forecasting

Nurhambali,

Angraini,

Fitrianto

2024

MJMS

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Gold is a form of investment known as a safe haven asset because of its stability in unstable market conditions. Gold price forecasting is important for investors as decisions making tool. This study aims to study the best long short--term memory (LSTM) hyperparameters (optimizer, learning rate, and epoch) from cross--validation for forecasting. LSTM, as part of deep learning methods, is developed based on a RNN widely used in time series forecasting. LSTM is superior compared to other methods for its ability to minimize errors and forecast for long--term periods. Walk--forward validation with sliding and extending window scenarios as a form of cross--validation is used to see the method's accuracy. The used data is sourced from the World Gold Council with daily data periods for January 1, 2003, to December 31, 2023. The optimizer used is Adam and RMSProp, each with learning rate values of 0.01, 0.001, 0.0001, and epoch values of 100, 500, 1000. The best model uses the Adam optimizer, a learning rate of 0.01, and an epoch value of 100 with a MAPE value of 0.4867\% in the validation process. Forecasting results show a tendency for gold prices to increase in the next eight years.

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Application Seasonal Autoregressive Integrated Moving Average to Forecast the Number of East Kalimantan Hotspots

Cited by 2 publications

References 1 publication

Forecasting prevalence of dengue hemorrhagic fever using ARIMA model in Sulawesi Tenggara Province, Indonesia

Forecasting prevalence of dengue hemorrhagic fever using ARIMA model in Sulawesi Tenggara Province, Indonesia

Implementation of Long Short-Term Memory for Gold Prices Forecasting

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