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Nearest‐Neighbour Methods For Time Series Analysis

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  • S. Yakowitz

Abstract

. The nearest‐neighbour method, because of its intuitively appealing nature and competitive theoretical properties, deserves consideration in time‐series applications akin to attention it has received lately in the i.i.d. case. Here it is shown that as a nonparametric regression device, like the kernel method, under the G2 mixing assumption, it converges in quadratic mean at the Stone‐optimal rate. In the closing sections, our methodology is extended to a broader pattern‐recognition context, and applied to hydrologic data.

Suggested Citation

  • S. Yakowitz, 1987. "Nearest‐Neighbour Methods For Time Series Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 8(2), pages 235-247, March.
    • Handle: RePEc:bla:jtsera:v:8:y:1987:i:2:p:235-247
    DOI: 10.1111/j.1467-9892.1987.tb00435.x
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    Cited by:

    1. Thomas Hellström & Kenneth Holmström, 2000. "The relevance of trends for predictions of stock returns," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(1), pages 23-34, March.
    2. Laurent Ferrara & Dominique Guégan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 186-199.
    3. Alexander Gleim & Nazarii Salish, 2022. "Forecasting Environmental Data: An example to ground-level ozone concentration surfaces," Papers 2202.03332, arXiv.org.
    4. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," PSE-Ecole d'économie de Paris (Postprint) halshs-00460472, HAL.
    5. Abul Abrar Masrur Ahmed & Ravinesh C. Deo & Sujan Ghimire & Nathan J. Downs & Aruna Devi & Prabal D. Barua & Zaher M. Yaseen, 2022. "Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model," Sustainability, MDPI, vol. 14(17), pages 1-27, September.
    6. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    7. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    8. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," PSE-Ecole d'économie de Paris (Postprint) halshs-00511979, HAL.
    9. Dominique Guegan & Justin Leroux, 2009. "Local Lyapunov Exponents: A new way to predict chaotic systems," PSE-Ecole d'économie de Paris (Postprint) halshs-00511996, HAL.

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