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Symmetrizing k-nn and Mutual k-nn Smoothers Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Selection of Window Length in Singular Spectrum Analysis of a Time Series

Authors : P. Unnikrishnan, V. Jothiprakash

Published in: Nonparametric Statistics

Publisher: Springer International Publishing

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Abstract

Singular Spectrum Analysis (SSA) is a promising non-parametric time series modelling technique that has proved to be successful in data preprocessing in diverse application fields. It is a window length-based method and the appropriate selection of window length plays a crucial role in the accuracy of SSA. However, there are no specific methods depicted in the literature about its selection. In this study, the method of SSA in time series analysis is presented in detail and a sensitivity analysis of window length is carried out based on an observed daily rainfall time series.

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previous chapter Strong Separability in Circulant SSA
next chapter Fourier-Type Monitoring Procedures for Strict Stationarity
Literature
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Jothiprakash, V., & Magar, R. B. (2012). Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data. Journal of Hydrology, 450–451, 293–307.
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Machiwal, D., & Jha, M. K. (2012). Hydrologic time series analysis: Theory and practice. Berlin: Springer.
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Priestley, M. (1981). Spectral analysis and time series. San Diego: Academic Press.
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Singh, V. P. (1988). Hydrologic systems: Vol. 1. Rainfall-runoff modeling. Eagle Wood Cliffs, NJ: Prentice Hall.
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Unnikrishnan, P., & Jothiprakash, V. (2015). Extraction of nonlinear rainfall trends using singular spectrum analysis. Journal of Hydrologic Engineering, 20, 501–507.
Metadata
Title
Selection of Window Length in Singular Spectrum Analysis of a Time Series
Authors
P. Unnikrishnan
V. Jothiprakash
Copyright Year
2018
Book
Nonparametric Statistics

Print ISBN: 978-3-319-96940-4

Electronic ISBN: 978-3-319-96941-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-96941-1_21

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