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INDUCTIVE AND MODEL-TREE-BASED APPROACHES FOR FORECASTING TEMPERATURE
Title
INDUCTIVE AND MODEL-TREE-BASED APPROACHES FOR FORECASTING TEMPERATURE.
Creator
Schauer, Alexis, Teegavarapu, Ramesh S. V., Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description
Inductive and model-tree (MT) approach-based models are developed and evaluated for forecasting mean, minimum and maximum monthly temperature in this study. The models are developed and tested using long-term historical temperature time series data derived from U.S. Historical Climatology Network at 22 sites located in the state of Florida. Inductive models developed include conceptually simple naïve models to multiple regression models utilizing lagged temperature values, sea surface...
Show moreInductive and model-tree (MT) approach-based models are developed and evaluated for forecasting mean, minimum and maximum monthly temperature in this study. The models are developed and tested using long-term historical temperature time series data derived from U.S. Historical Climatology Network at 22 sites located in the state of Florida. Inductive models developed include conceptually simple naïve models to multiple regression models utilizing lagged temperature values, sea surface temperatures (SSTs), correction factors derived using historical data. A global model using data from all the sites is also developed. The performances of the models were evaluated using observed temperature records and several error and performance measures. A composite measure combining multiple error and performance measures is developed to select the best model. MT approach-based and regression models with SSTs and correction factors along with lagged temperature values are found to be best models for forecasting temperature based on assessments of composite measures and error diagnostics.
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Date Issued
2021
PURL
http://purl.flvc.org/fau/fd/FA00013856
Subject Headings
Temperature, Forecasting--Mathematical models
Format
Document (PDF)
Causality between stock prices and exchange rates: A case of the United States
Title
Causality between stock prices and exchange rates: A case of the United States.
Creator
Ozair, Amber., Florida Atlantic University, Yuhn, Ky-hyang
Abstract/Description
This thesis investigates the direction of causality as well as short-run dynamics and long-run equilibrium relationship between stock prices and exchange rates using quarterly data for the period 1960:1--2004:4. The studies apply techniques of the unit root, cointegration and Standard Granger causality tests to examine the relationship between these two financial variables. The empirical results reveal that there is no causal linkage and no cointegration between the stock prices and exchange...
Show moreThis thesis investigates the direction of causality as well as short-run dynamics and long-run equilibrium relationship between stock prices and exchange rates using quarterly data for the period 1960:1--2004:4. The studies apply techniques of the unit root, cointegration and Standard Granger causality tests to examine the relationship between these two financial variables. The empirical results reveal that there is no causal linkage and no cointegration between the stock prices and exchange rates as suggested under Traditional and Portfolio approaches. The results support the view that the semi-strong form of EMH holds true for the U.S. financial markets.
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Date Issued
2006
PURL
http://purl.flvc.org/fcla/dt/13393
Subject Headings
Econometric models, Business forecasting--Mathematical models, Efficient market theory, Stock exchanges--Mathematical models
Format
Document (PDF)
Forecasting foreign exchange rates using neural networks
Title
Forecasting foreign exchange rates using neural networks.
Creator
Talati, Amit H., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Time series is a phenomena which appears in the financial world in various forms. One of the objectives of time series is to forecast the future based on the past. The goal of this thesis is to use foreign exchange time series, and predict its future values and trends using neural networks. The thesis covers background work in this area and discusses the results obtained by other researchers. A neural network is then developed to predict the future values of the USD/GBP and USD/DEM exchange...
Show moreTime series is a phenomena which appears in the financial world in various forms. One of the objectives of time series is to forecast the future based on the past. The goal of this thesis is to use foreign exchange time series, and predict its future values and trends using neural networks. The thesis covers background work in this area and discusses the results obtained by other researchers. A neural network is then developed to predict the future values of the USD/GBP and USD/DEM exchange rates. Both single-step and iterated multi-step predictions are considered. The performance of neural networks strongly depends on the inputs supplied. The effect of the changes in the number of inputs is also considered, and a method suggested for deciding on the optimum number. The forecasting of foreign exchange rates is a challenge because of the dynamic nature of the FOREX market and its dependencies on world events. The tool used for building the neural network and validating the approach is "Brainmaker".
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Date Issued
2000
PURL
http://purl.flvc.org/fcla/dt/12699
Subject Headings
Foreign exchange rates--Mathmematical models, Foreign exchange--Forecasting--Mathematical models, Neural networks (Computer science)
Format
Document (PDF)
Sensitivity analysis of predictive data analytic models to attributes
Title
Sensitivity analysis of predictive data analytic models to attributes.
Creator
Chiou, James, Zhu, Xingquan, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Classification algorithms represent a rich set of tools, which train a classification model from a given training and test set, to classify previously unseen test instances. Although existing methods have studied classification algorithm performance with respect to feature selection, noise condition, and sample distributions, our existing studies have not addressed an important issue on the classification algorithm performance relating to feature deletion and addition. In this thesis, we...
Show moreClassification algorithms represent a rich set of tools, which train a classification model from a given training and test set, to classify previously unseen test instances. Although existing methods have studied classification algorithm performance with respect to feature selection, noise condition, and sample distributions, our existing studies have not addressed an important issue on the classification algorithm performance relating to feature deletion and addition. In this thesis, we carry out sensitive study of classification algorithms by using feature deletion and addition. Three types of classifiers: (1) weak classifiers; (2) generic and strong classifiers; and (3) ensemble classifiers are validated on three types of data (1) feature dimension data, (2) gene expression data and (3) biomedical document data. In the experiments, we continuously add redundant features to the training and test set in order to observe the classification algorithm performance, and also continuously remove features to find the performance of the underlying classifiers. Our studies draw a number of important findings, which will help data mining and machine learning community under the genuine performance of common classification algorithms on real-world data.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004274, http://purl.flvc.org/fau/fd/FA00004274
Subject Headings
Data mining, Forecasting -- Mathematical models, Social sciences -- Statistical methods, Ubiquitous computing
Format
Document (PDF)
SHORT-TERM FORECASTING METHODS WITH REFERENCE TO MONROE COUNTY
Title
SHORT-TERM FORECASTING METHODS WITH REFERENCE TO MONROE COUNTY.
Creator
PEREZ, JOSE RAMON., Florida Atlantic University, Stronge, William B., College of Business, Department of Economics
Abstract/Description
This thesis is a study of short-term forecasting models within the reference area of Monroe County. Its main concern being the nature of the models, and the accuracy of predictions rather than the actual forecast. The results are of interest to the student of forecasting and of Monroe County. A proxy model is introduced as an alternative to other methods of regional analysis with the intention of inducing further research on the field.
Date Issued
1972
PURL
http://purl.flvc.org/fcla/dt/13515
Subject Headings
Economic forecasting--Mathematical models, Economic forecasting--Florida--Monroe County, Monroe County (Fla)--Economic conditions--Mathematical models
Format
Document (PDF)