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All Journal E-Jurnal Matematika
RATNA SARI WIDIASTUTI
3Program Studi Matematika, FMIPA, Universitas Udayana
Mathematics

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PERAMALAN DURASI ETHEREUM MENGGUNAKAN MODEL AUTOREGRESSIVE CONDITIONAL DURATION I WAYAN SUMARJAYA; RENOVAR JOJOR DELIMA SIMANULLANG; RATNA SARI WIDIASTUTI
E-Jurnal Matematika Vol. 14 No. 3 (2025)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2025.v14.i03.p484

Abstract

Forecasting is the process of estimating future events using past data. Financial time series forecasting often prioritizes stock price variables. Apart from the stock price variable, inter-transaction time or duration is also an important variable to predict, because the timing of changes in financial prices cannot be predicted. Duration modeling and forecasting can be done using the autoregressive conditional duration (ACD) model. In this research, modeling and forecasting using the ACD model was carried out on Ethereum. This research aims to predict the duration of Ethereum in order to help traders know the time needed to reach the next price change. Several ACD models with four distributions, i.e., exponential, Weibull, Burr, and generalized gamma were fit to the Ethereum duration. The research results suggest that the Burr-ACD model produces the smallest AIC value compared to other distributed ACD models. However, the forecast results using the Burr-ACD models show increasing duration and hence are less accurate. The generalized gamma-ACD (2,2) model was then chosen as an alternative for forecasting Ethereum duration, showing that Ethereum duration forecast results are less than one second, which indicates the high frequency of transactions that occur on Ethereum.
Co-Authors I Wayan Sumarjaya RENOVAR JOJOR DELIMA SIMANULLANG