You are here
Wind speed analysis for Lake Okeechobee
- Date Issued:
- 2002
- Summary:
- In this thesis, we analyze wind speeds collected by South Florida Water Management District at stations L001, L005, L006 and LZ40 in Lake Okeechobee from January 1995 to December 2000. There are many missing values and out-liers in this data. To impute the missing values, three different methods are used: Nearby window average imputation, Jones imputation using Kalman filter, and EM algorithm imputation. To detect outliers and remove impacts, we use ARIMA models of time series. Innovational and additive outliers are considered. It turns out that EM algorithm imputation is the best method for our wind speed data set. After imputing missing values, detecting outliers and removing the impacts, we obtain the best models for all four stations. They are all in the form of seasonal ARIMA(2, 0, 0) x (1, 0, 0)24 for the hourly wind speed data.
Title: | Wind speed analysis for Lake Okeechobee. |
101 views
29 downloads |
---|---|---|
Name(s): |
Hu, Mingyan Florida Atlantic University, Degree grantor Qian, Lianfen, Thesis advisor Charles E. Schmidt College of Science Department of Mathematical Sciences |
|
Type of Resource: | text | |
Genre: | Electronic Thesis Or Dissertation | |
Issuance: | monographic | |
Date Issued: | 2002 | |
Publisher: | Florida Atlantic University | |
Place of Publication: | Boca Raton, FL | |
Physical Form: | application/pdf | |
Extent: | 72 p. | |
Language(s): | English | |
Summary: | In this thesis, we analyze wind speeds collected by South Florida Water Management District at stations L001, L005, L006 and LZ40 in Lake Okeechobee from January 1995 to December 2000. There are many missing values and out-liers in this data. To impute the missing values, three different methods are used: Nearby window average imputation, Jones imputation using Kalman filter, and EM algorithm imputation. To detect outliers and remove impacts, we use ARIMA models of time series. Innovational and additive outliers are considered. It turns out that EM algorithm imputation is the best method for our wind speed data set. After imputing missing values, detecting outliers and removing the impacts, we obtain the best models for all four stations. They are all in the form of seasonal ARIMA(2, 0, 0) x (1, 0, 0)24 for the hourly wind speed data. | |
Identifier: | 9780493547435 (isbn), 12883 (digitool), FADT12883 (IID), fau:9757 (fedora) | |
Degree granted: | Thesis (M.S.)--Florida Atlantic University, 2002. | |
Collection: | FAU Electronic Theses and Dissertations Collection | |
Note(s): | Charles E. Schmidt College of Science | |
Subject(s): |
Winds--Speed--Florida--Okeechobee, Lake Okeechobee, Lake (Fla )--Environmental conditions |
|
Held by: | Florida Atlantic University Libraries | |
Persistent Link to This Record: | http://purl.flvc.org/fcla/dt/12883 | |
Sublocation: | Digital Library | |
Use and Reproduction: | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
Host Institution: | FAU | |
Is Part of Series: | Florida Atlantic University Digital Library Collections. |