Nonlinear time series modeling of some Canadian river flow data
Thesis (M.A.S.)--Memorial University of Newfoundland, 2000. Mathematics and Statistics Bibliography: leaves 71-73 In hydrology the ability to model the average daily river flow for rivers plays an important role in the prediction of possible disasters such as flooding. The analysis of data and the a...
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ftmemorialunivdc:oai:collections.mun.ca:theses3/64566 2023-05-15T17:23:32+02:00 Nonlinear time series modeling of some Canadian river flow data Batten, Douglas James, 1973- Memorial University of Newfoundland. Dept. of Mathematics and Statistics 2000 ix, 93 leaves Image/jpeg; Application/pdf http://collections.mun.ca/cdm/ref/collection/theses3/id/64566 eng eng Electronic Theses and Dissertations (8.90 MB) -- http://collections.mun.ca/PDFs/theses/Batten_DouglasJames.pdf a1476526 http://collections.mun.ca/cdm/ref/collection/theses3/id/64566 The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries Linear models (Statistics) Hydrologic models Text Electronic thesis or dissertation 2000 ftmemorialunivdc 2015-08-06T19:18:05Z Thesis (M.A.S.)--Memorial University of Newfoundland, 2000. Mathematics and Statistics Bibliography: leaves 71-73 In hydrology the ability to model the average daily river flow for rivers plays an important role in the prediction of possible disasters such as flooding. The analysis of data and the accuracy of predictions rely on fitting suitable models to such data. In this practicum we investigate nonlinear time series modeling and In particular we study the theory of two approaches to model such time series. One approach assumes the underlying random structure of the time series is bilinear. The second approach uses wavelet smoothing techniques to decompose the time series into a wavelet smoothed component and a random component. The random component is then modeled by a suitable linear or bilinear process. By investigating the structure of the autocorrelation and third order cumulants, we find that the pure bilinear process is best for the data sets under study. Models were fitted to six time series data sets based on the average daily river flow variable for six rivers in Canada using both approaches. A simulation study was conducted to establish the suitability of the models by comparing its performance to the original time series. The bilinear approach was not favorable in modeling average daily river flow. However, the wavelet methodology illustrated an attractive technique to model such a time series. Thesis Newfoundland studies University of Newfoundland Memorial University of Newfoundland: Digital Archives Initiative (DAI) Canada |
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Memorial University of Newfoundland: Digital Archives Initiative (DAI) |
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ftmemorialunivdc |
language |
English |
topic |
Linear models (Statistics) Hydrologic models |
spellingShingle |
Linear models (Statistics) Hydrologic models Batten, Douglas James, 1973- Nonlinear time series modeling of some Canadian river flow data |
topic_facet |
Linear models (Statistics) Hydrologic models |
description |
Thesis (M.A.S.)--Memorial University of Newfoundland, 2000. Mathematics and Statistics Bibliography: leaves 71-73 In hydrology the ability to model the average daily river flow for rivers plays an important role in the prediction of possible disasters such as flooding. The analysis of data and the accuracy of predictions rely on fitting suitable models to such data. In this practicum we investigate nonlinear time series modeling and In particular we study the theory of two approaches to model such time series. One approach assumes the underlying random structure of the time series is bilinear. The second approach uses wavelet smoothing techniques to decompose the time series into a wavelet smoothed component and a random component. The random component is then modeled by a suitable linear or bilinear process. By investigating the structure of the autocorrelation and third order cumulants, we find that the pure bilinear process is best for the data sets under study. Models were fitted to six time series data sets based on the average daily river flow variable for six rivers in Canada using both approaches. A simulation study was conducted to establish the suitability of the models by comparing its performance to the original time series. The bilinear approach was not favorable in modeling average daily river flow. However, the wavelet methodology illustrated an attractive technique to model such a time series. |
author2 |
Memorial University of Newfoundland. Dept. of Mathematics and Statistics |
format |
Thesis |
author |
Batten, Douglas James, 1973- |
author_facet |
Batten, Douglas James, 1973- |
author_sort |
Batten, Douglas James, 1973- |
title |
Nonlinear time series modeling of some Canadian river flow data |
title_short |
Nonlinear time series modeling of some Canadian river flow data |
title_full |
Nonlinear time series modeling of some Canadian river flow data |
title_fullStr |
Nonlinear time series modeling of some Canadian river flow data |
title_full_unstemmed |
Nonlinear time series modeling of some Canadian river flow data |
title_sort |
nonlinear time series modeling of some canadian river flow data |
publishDate |
2000 |
url |
http://collections.mun.ca/cdm/ref/collection/theses3/id/64566 |
geographic |
Canada |
geographic_facet |
Canada |
genre |
Newfoundland studies University of Newfoundland |
genre_facet |
Newfoundland studies University of Newfoundland |
op_source |
Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries |
op_relation |
Electronic Theses and Dissertations (8.90 MB) -- http://collections.mun.ca/PDFs/theses/Batten_DouglasJames.pdf a1476526 http://collections.mun.ca/cdm/ref/collection/theses3/id/64566 |
op_rights |
The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. |
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1766113091953623040 |