Autocorrelation for time series with linear trend

dc.contributor.authorKamalov, Firuz
dc.contributor.authorThabtah, Fadi
dc.contributor.authorGurrib, Ikhlaas
dc.date.accessioned2021-12-29T10:37:55Z
dc.date.available2021-12-29T10:37:55Z
dc.date.copyright© 2021
dc.date.issued2021-09-29
dc.descriptionThis 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/10.1109/3ICT53449.2021.9581809en_US
dc.description.abstractThe 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.citationKamalov, 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/10.1109/3ICT53449.2021.9581809en_US
dc.identifier.isbn978-166544032-5
dc.identifier.urihttp://hdl.handle.net/20.500.12519/486
dc.identifier.urihttps://doi.org/10.1109/3ICT53449.2021.9581809
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relationAuthors 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.ispartofseries2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021;
dc.rightsPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.
dc.rights.holderCopyright : 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.urihttps://www.ieee.org/publications/rights/rights-policies.html
dc.subjectacfen_US
dc.subjectarimaen_US
dc.subjectautocorrelationen_US
dc.subjectforecastingen_US
dc.subjectlinear trenden_US
dc.subjecttime seriesen_US
dc.titleAutocorrelation for time series with linear trenden_US
dc.typeConference Paperen_US
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