Near-Ideal Predictors and Causal Filters for Discrete-Time Signals-Reference-Cited by-同舟云学术

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Near-Ideal Predictors and Causal Filters for Discrete-Time Signals

Published:2023-07 Issue:2 Volume:59 Page:99-114
ISSN:0032-9460
Container-title:Problems of Information Transmission
language:en
Short-container-title:Probl Inf Transm

Author:

Dokuchaev N. G.

Publisher

Pleiades Publishing Ltd

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Link

https://link.springer.com/content/pdf/10.1134/S0032946023020035.pdf

Reference15 articles.

1. Butzer, P.L. and Stens, R.L., Linear Prediction by Samples from the Past, Advanced Topics in Shannon Sampling and Interpolation Theory, Marks, R.J., Ed., New York: Springer, 1993, pp. 157–183. https://doi.org/10.1007/978-1-4613-9757-1_5

2. Higgins, J.R., Sampling Theory in Fourier and Signal Analysis: Foundations, Oxford: Clarendon; New York: Oxford Univ. Press, 1996.

3. Li, Z., Han, J., and Song, Y., On the Forecasting of High-Frequency Financial Time Series Based on ARIMA Model Improved by Deep Learning, J. Forecast., 2020, vol. 39, no. 7, pp. 1081–1097. https://doi.org/10.1002/for.2677

4. Luo, S. and Tian, C., Financial High-Frequency Time Series Forecasting Based on Sub-step Grid Search Long Short-Term Memory Network, IEEE Access, 2020, vol. 8, pp. 203183–203189. https://doi.org/10.1109/ACCESS.2020.3037102

5. Knab, J.J., Interpolation of Band-Limited Functions Using the Approximate Prolate Series, IEEE Trans. Inform. Theory, 1979, vol. 25, no. 6, pp. 717–720. https://doi.org/10.1109/TIT.1979.1056115

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