Autocorrelation for time series with linear trend

dc.contributor.author Kamalov, Firuz
dc.contributor.author Thabtah, Fadi
dc.contributor.author Gurrib, Ikhlaas
dc.date.accessioned 2021-12-29T10:37:55Z
dc.date.available 2021-12-29T10:37:55Z
dc.date.copyright © 2021
dc.date.issued 2021-09-29
dc.description This conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021 (2021), available online at: https://doi.org/10.1109/3ICT53449.2021.9581809 en_US
dc.description.abstract The autocorrelation function (ACF) is a fundamental concept in time series analysis including financial forecasting. In this note, we investigate the properties of the sample ACF for a time series with linear trend. In particular, we show that the sample ACF of the time series approaches 1 for all lags as the number of time steps increases. The theoretical results are supported by numerical experiments. Our result helps researchers better understand the ACF patterns and make correct ARMA selection. © 2021 IEEE. en_US
dc.identifier.citation Kamalov, F., Thabtah, F., & Gurrib, I. (2021). Autocorrelation for time series with linear trend. Paper presented at the 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021, 181-185. https://doi.org/10.1109/3ICT53449.2021.9581809 en_US
dc.identifier.isbn 978-166544032-5
dc.identifier.uri http://hdl.handle.net/20.500.12519/486
dc.identifier.uri https://doi.org/10.1109/3ICT53449.2021.9581809
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation Authors Affiliations : Kamalov, F., Canadian University Dubai, Department of Electrical Engineering, Dubai, United Arab Emirates; Thabtah, F., School of Digital Technologies, Manukau Institute of Technology, Manukau, New Zealand; Gurrib, I., Canadian University Dubai, Department of Finance, Dubai, United Arab Emirates
dc.relation.ispartofseries 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021;
dc.rights Permission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.
dc.rights.holder Copyright : Copyright : © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights.uri https://www.ieee.org/publications/rights/rights-policies.html
dc.subject acf en_US
dc.subject arima en_US
dc.subject autocorrelation en_US
dc.subject forecasting en_US
dc.subject linear trend en_US
dc.subject time series en_US
dc.title Autocorrelation for time series with linear trend en_US
dc.type Conference Paper en_US
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