Prediction and tracking of long-range-dependent sequences |
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| Authors: | Erhan Bayraktar H. Vincent Poor Raghuveer Rao |
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| Affiliation: | aDepartment of Mathematics, University of Michigan, 525 East University, Ann Arbor, MI 48109, USA;bDepartment of Electrical Engineering, Princeton University, Princeton, NJ 08544, USA;cElectrical Engineering Department, Rochester Institute of Technology, Rochester, NY 14623, USA |
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| Abstract: | Long-range-dependent (LRD) sequences have been found to be of importance in various fields such as telecommunications, signal processing and finance. Since the history of an LRD sequence has significant impact on the present values, it is expected that accurate prediction and tracking of these sequences are easier than of short-range-dependent sequences. The purpose of this paper is to verify whether distant observations in the past might increase the performance of a constrained tracker significantly when this information from the past is used in combination with recent observations. |
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| Keywords: | Fractional Gaussian noise Long range dependence Tracking problems |
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