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Wind speed analysis for Lake Okeecho...

Hu, Mingyan.

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Wind speed analysis for Lake Okeechobee (Florida).

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Wind speed analysis for Lake Okeechobee (Florida)./
作者:	Hu, Mingyan.
面頁冊數:	61 p.
附註:	Source: Masters Abstracts International, Volume: 40-05, page: 1246.
Contained By:	Masters Abstracts International40-05.
標題:	Mathematics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1407889
ISBN:	0493547436

Wind speed analysis for Lake Okeechobee (Florida).
Hu, Mingyan.

Wind speed analysis for Lake Okeechobee (Florida). - 61 p.

Source: Masters Abstracts International, Volume: 40-05, page: 1246.

Thesis (M.S.)--Florida Atlantic University, 2002.

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.

ISBN: 0493547436Subjects--Topical Terms:

515831
Mathematics.

Wind speed analysis for Lake Okeechobee (Florida).
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502 $a Thesis (M.S.)--Florida Atlantic University, 2002.
520 $a 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.
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