Autocorrelation for time series with linear trend

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Date
2021-09-29
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Publisher
Institute of Electrical and Electronics Engineers Inc.
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.
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
Keywords
acf, arima, autocorrelation, forecasting, linear trend, time series
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
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http://hdl.handle.net/20.500.12519/486
 https://doi.org/10.1109/3ICT53449.2021.9581809
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Department of Electrical Engineering