Time series forecasting and data mining of tornadoes in the United States
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 through 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. Naive forecasts, seasonal naive 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. Secondly, data mining models, namely multiple linear regression, ridge regression, the lasso, principal components regression, partial least squares regression, generalized additive models (GAMs) decision trees, bagging, random forests and boosting are applied to the dataset of 64519 tornado events over a time period of 66 years within the United States, with the property damage as the response variable, and other features as independent variables. The primary goal is estimation and the secondary goal is inference. After the training and 10 fold cross validation of different data mining models, boosting is found to generate the most accurate estimates for property damage. The results of inference from the lasso indicate that the property damage increases by $53819 for every 0.1 mile increase in the tornado length, and by $9780 for every 1 foot increase in tornado width.