ARIMA Intervention Model for Measuring the Impact of the Lobster Seeds Fishing and Export Ban Policy on the Indonesian Lobster Export

Ilmiah, Ria Dhotul; Oktora, Siskarossa Ika

doi:10.1088/1742-6596/2123/1/012011

Cited by 2 publications

(2 citation statements)

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“…These variables have only the values 0 and 1 to represent the non-occurrence and the occurence of an intervention (Buckley et al, 2020). The step function is a representation of an intervention that takes place at time T and is assumed to have long-term (permanent) effects (Ilmiah & Oktora, 2021). The intervention at time T with a step function at time t (𝑆 𝑡 (𝑇) ) can be written as…”

Section: Intervention Analysismentioning

confidence: 99%

ARIMA Time Series Modeling with the Addition of Intervention and Outlier Factors on Inflation Rate in Indonesia

Utami,

Huda,

Imro'ah

2024

JTAM

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Extreme events in a time series model can be detected when the precise timing of the event, known as the intervention, is known. When the exact timing of an event is unknown, it is referred to as an outlier. If these factors are neglected, the model's accuracy will be affected. To overcome this situation, it is possible to add the intervention or outlier factor into the time series model. This study proposes the combination of intervention and outlier analysis in time series models, especially ARIMA. It is intended to minimize the residuals and increase the accuracy of the model so that it is suitable for forecasting. Using the data of inflation rate in Indonesia, the conflict between Russia and Ukraine was used as an intervention factor in this case. Pre-intervention data (before February 2022) is used to construct the ARIMA model (1st model). After that, the modeling process continued by adding the intervention factor to the ARIMA model. The effect caused by the intervention allows an outlier to appear, so the process is continued by adding the outlier factor, called an additive outlier, into the model before (2nd model). The MAPE for the first and second models is 7.96% and 7.57%, respectively. The finding of this research shows that the ARIMA model with intervention and outlier factors, named as the 2nd model, is the best model. This study shows that combining the intervention and outlier factors into ARIMA model can improve the accuracy. The forecasting of the inflation rate in Indonesia for one period ahead in 2023 is in the range of 2.06%.

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Section: Intervention Analysismentioning

confidence: 99%

ARIMA Time Series Modeling with the Addition of Intervention and Outlier Factors on Inflation Rate in Indonesia

Utami,

Huda,

Imro'ah

2024

JTAM

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“…Hasil peramalan menunjukkan bahwa pertumbuhan ekspor di daerah tersebut akan menurun. Penelitian lainnya dilakukan oleh Ruslan, Harahap, & Sembiring (2013); Başer et al (2018); Dave et al, (2021); Ilmiah & Oktora (2021); Lu (2015); Zuhdi et al, 2022).…”

Section: Pendahuluanunclassified

Peramalan Ekspor Karet Provinsi Jambi dengan Model Seasonal ARIMA

Atmanegara

2022

JIITUJ

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The industrial sector contributed the most to total non-oil and gas exports in Jambi Province. Rubber and its processed products are the main export commodities in the industrial sector. The export value of rubber and its products increased in 2017 but decreased again from 2018 to 2020. The decline in exports harmed the economy in Jambi Province. It is necessary to predict the export value of rubber and its products in the coming period. Forecasting is done on time series data using the ARIMA and SARIMA models. The best model for modeling the export value of rubber and its products in Jambi Province is SARIMA (0,1,1)(0,0,1)12. It is predicted that the export value of rubber and its processed products will decrease by around 4 percent in 2022. And decrease by around 5 percent in 2023. The results of this forecast can be used as an early warning in making policies related to exports in the coming period.

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ARIMA Intervention Model for Measuring the Impact of the Lobster Seeds Fishing and Export Ban Policy on the Indonesian Lobster Export

Cited by 2 publications

References 0 publications

ARIMA Time Series Modeling with the Addition of Intervention and Outlier Factors on Inflation Rate in Indonesia

ARIMA Time Series Modeling with the Addition of Intervention and Outlier Factors on Inflation Rate in Indonesia

Peramalan Ekspor Karet Provinsi Jambi dengan Model Seasonal ARIMA

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