Automatic forecasting for univariate time series with long memory property

In applications of time series forecasting, it is necessary to model the data automatically and obtain the corresponding forecast values, possibly in real-time, using various methods. A number of automatic algorithms for modeling univariate time series data are available in the literature. This pape...

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Bibliographic Details
Main Authors: Rosadi, Dedi, Peiris, Shelton, Rosmalawati, Meri Andani
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Forecasting
Mathematical models
Open source software
Time series
Online Access:Get full text
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Summary:In applications of time series forecasting, it is necessary to model the data automatically and obtain the corresponding forecast values, possibly in real-time, using various methods. A number of automatic algorithms for modeling univariate time series data are available in the literature. This paper is restricted to a study on automatic forecasting algorithms for time series data which contain long memory property using automatic Fractional Autoregressive Integrated Moving Average (FARIMA/ARFIMA) model. The algorithms are implemented through open-source software R. We provide empirical application to illustrate and show applicability of the proposed methods and tools using real data.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0126617