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- W2789634017 abstract "The objectives of this thesis are the time series forecasting of tornadoes in Oklahoma, estimating property damage due to tornadoes in the United States using multivariate data mining models, and generating inferences from past tornado events in the United States though data mining. Firstly, univariate time series modeling is applied to generate monthly forecasts for the estimated property damage due to the tornado event, the length of the tornado, the width of the tornado, and the tornado strike location, which is determined by the beginning latitude and beginning longitude of the tornado event. Naïve forecasts, seasonal naïve forecasts, trailing moving average, the Holt-Winters model, exponential smoothing, linear regression, ARIMA, and artificial neural networks are the time series models investigated in this research. In each case, after training and validating different time series models, the best performing model is selected to generate monthly forecasts with prediction intervals from January 2015 to December 2015. After comparing the monthly forecasts with the true values for the year 2015, it is observed that the true values lie within the forecasted 95% prediction intervals for each time series on 59 out of 60 occasions." @default.
- W2789634017 created "2018-03-29" @default.
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- W2789634017 date "2021-05-10" @default.
- W2789634017 modified "2023-10-05" @default.
- W2789634017 title "Time series forecasting and data mining of tornadoes in the United States" @default.
- W2789634017 doi "https://doi.org/10.17760/d20239505" @default.
- W2789634017 hasPublicationYear "2021" @default.
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