ARFIMA and ARTFIMA Processes in Time Series with Applications

Kuldeep Kumar, Priya Chaturvedi

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

Abstract

The objective of this study is to discuss different fractional time series processes, such as Autoregressive Fractional Integrated Moving Average (ARFIMA), which are also called long-memory or persistent models. Due to the very nature of long memories, these processes have an obvious bearing on forecast and are worthy of study. Another variant of these processes is Autoregressive Temporal Fractional Integrated Moving (ARTFIMA), which applies a tempered fractional difference to the standard Autoregressive Moving Average (ARMA) process. Various estimation and forecast methods are discussed, and utility of these models is illustrated with application to real data sets.
Original languageEnglish
Title of host publicationRecent Advances in Time Series Forecasting
EditorsDinesh Bisht, Mangey Ram
Place of PublicationBoca Raton
PublisherCRC Press
Chapter7
Pages99-110
Number of pages11
Edition1
ISBN (Electronic)9781003102281
ISBN (Print)978-0-367-60869-9, 978-0-367-60775-3
DOIs
Publication statusE-pub ahead of print - 8 Sep 2021

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