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All Journal SAINSMAT SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam
Mangkona, Andi Ilham Azhar
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Biochemistry, Genetics & Molecular Biology Chemistry Decision Sciences, Operations Research & Management Education Physics

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Intervention Analysis In Time Series Data For Forecasting Bbri Stock Prices Mangkona, Andi Ilham Azhar; Aswi, Aswi; Ruliana, Ruliana
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 1 (2025): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat141670452025

Abstract

Intervention model analysis is a statistical technique used to assess the impact of an intervention event, caused by internal or external factors, on a time series dataset. The primary goal of this analysis is to quantify the magnitude and duration of the effects on the time series. Intervention models are typically divided into two types: the step function and the pulse function. The step function represents an intervention event with a long-term influence, while the pulse function captures the effects of an intervention within a specific time span. This study examines the stock price data of BBRI from March 2017 to June 2020, with the intervention point identified as the onset of COVID-19 in Indonesia, specifically during the first week of March (t = 155). ARIMA modeling was applied to pre-intervention data to determine the order of intervention (b, s, r). The analysis concluded that the best-fitting model was ARIMA (2, 1, 0), with the intervention order characterized by a step function where b = 0, s = 2, and r = 0. The accuracy of the forecasting results was evaluated using the Mean Absolute Percentage Error (MAPE), which yielded a value of 8.48%.
Intervention Analysis In Time Series Data For Forecasting Bbri Stock Prices Mangkona, Andi Ilham Azhar; Aswi, Aswi; Ruliana, Ruliana
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 1 (2025): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat141670452025

Abstract

Intervention model analysis is a statistical technique used to assess the impact of an intervention event, caused by internal or external factors, on a time series dataset. The primary goal of this analysis is to quantify the magnitude and duration of the effects on the time series. Intervention models are typically divided into two types: the step function and the pulse function. The step function represents an intervention event with a long-term influence, while the pulse function captures the effects of an intervention within a specific time span. This study examines the stock price data of BBRI from March 2017 to June 2020, with the intervention point identified as the onset of COVID-19 in Indonesia, specifically during the first week of March (t = 155). ARIMA modeling was applied to pre-intervention data to determine the order of intervention (b, s, r). The analysis concluded that the best-fitting model was ARIMA (2, 1, 0), with the intervention order characterized by a step function where b = 0, s = 2, and r = 0. The accuracy of the forecasting results was evaluated using the Mean Absolute Percentage Error (MAPE), which yielded a value of 8.48%.
Co-Authors Aswi, Aswi Ruliana Ruliana, Ruliana