Time Series Model Analysis Using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) for E-wallet Transactions during a Pandemic

Usman Abbas Yakubu; Moch Panji Agung Saputra

doi:10.47194/ijgor.v3i3.168

Authors

Usman Abbas Yakubu Department of Mathematics, Yusuf Maitama Sule University, Kano, Nigeria
Moch Panji Agung Saputra
moch16006@mail.unpad.ac.id
Department of Mathematics, Universitas padjadjaran

Keywords:

E-wallet, time series, ACF, PACF.

Abstract

The use of e-wallet can be accessed easily via the internet, this can create a positive impact for economic stability after the Covid-19 pandemic. This can move the wheels of the community's economy, through online shopping and the use of e-wallet among the public. The use of a number of digital services in Indonesia has increased during the Covid-19 pandemic. The first position is occupied by e-commerce and the second position is occupied by digital wallets which increased by 65%. Based on data from the increasing number of e-wallet service users in Indonesia. There are several forms of e-wallet that have a large scale, such as GoPay, OVO, Tokopedia, and Bukalapak. Several types of e-wallets can be analyzed for time series models, so that they can help project e-wallet transactions in the post-pandemic future. The method for obtaining the time series model is using the Autocorrelation Function (ACF) and the Patial Autocorrelation Function (PACF).

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