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Tidal and low-frequency net displacement in a coastal lagoon
Title
Tidal and low-frequency net displacement in a coastal lagoon.
Creator
Smith, Ned P.
Date Issued
1983
PURL
http://purl.flvc.org/FCLA/DT/3174010
Subject Headings
Lagoons, Tides, Bays, Coastal ecology, Time-series analysis
Format
Document (PDF)
Coastal upwelling off the central Florida Atlantic coast: cold near-shore waters during summer months surprise many divers
Title
Coastal upwelling off the central Florida Atlantic coast: cold near-shore waters during summer months surprise many divers.
Creator
Pitts, Patrick A., Harbor Branch Oceanographic Institute
Date Issued
1993
PURL
http://purl.flvc.org/fau/fd/FA00007347
Subject Headings
Atlantic coast, Continental shelf--Florida, Upwelling (Oceanography), Summer, Time-series analysis
Format
Document (PDF)
Tidal and long-term flow through two tidal channels connecting southern Biscayne Bay and Card Sound with Atlantic shelf waters
Title
Tidal and long-term flow through two tidal channels connecting southern Biscayne Bay and Card Sound with Atlantic shelf waters.
Creator
Pitts, Patrick A., Harbor Branch Oceanographic Institute
Date Issued
2000
PURL
http://purl.flvc.org/FCLA/DT/3172800
Subject Headings
Tidal currents, Winds, Runoff, Spectral theory (Mathematics), Time-series analysis
Format
Document (PDF)
Meteorological forcing of coastal waters by the inverse barometer effect
Title
Meteorological forcing of coastal waters by the inverse barometer effect.
Creator
Smith, Ned P.
Date Issued
1979
PURL
http://purl.flvc.org/FCLA/DT/3172952
Subject Headings
Ocean-atmosphere interaction, Atmospheric pressure, Time-series analysis, Coasts, Mexico, Gulf of
Format
Document (PDF)
Numerical simulation of bay-shelf exchanges with a one-dimensional model
Title
Numerical simulation of bay-shelf exchanges with a one-dimensional model.
Creator
Smith, Ned P.
Date Issued
1985
PURL
http://purl.flvc.org/fau/fd/FA00007072
Subject Headings
Corpus Christi Bay (Tex.), Tides, Continental shelf, Time-series analysis, Simulation, Water levels, Bays
Format
Document (PDF)
Near-bottom cross-shelf heat flux along central Florida's Atlantic shelf break: winter months
Title
Near-bottom cross-shelf heat flux along central Florida's Atlantic shelf break: winter months.
Creator
Smith, Ned P.
Date Issued
1987
PURL
http://purl.flvc.org/FCLA/DT/3172970
Subject Headings
Ocean currents, Ocean temperature, Heat --Transmission, Seasons, Time-series analysis
Format
Document (PDF)
Temporal and spatial variability in longshore motion along the Texas Gulf coast
Title
Temporal and spatial variability in longshore motion along the Texas Gulf coast.
Creator
Smith, Ned P.
Date Issued
1980
PURL
http://purl.flvc.org/FCLA/DT/3174217
Subject Headings
Gulf Region (Tex.), Coasts, Littoral drift, Space and time, Time-series analysis
Format
Document (PDF)
Observations of steady and seasonal salt, heat, and mass transport through a tidal channel
Title
Observations of steady and seasonal salt, heat, and mass transport through a tidal channel.
Creator
Smith, Ned P.
Date Issued
1995
PURL
http://purl.flvc.org/FCLA/DT/3340503
Subject Headings
Tidal currents, Water temperature, Salinity, Time-series analysis, Exuma (Bahamas)
Format
Document (PDF)
TIME SERIES ANALYSIS OF INFLATION AND UNEMPLOYMENT 1948 - 1981
Title
TIME SERIES ANALYSIS OF INFLATION AND UNEMPLOYMENT 1948 - 1981.
Creator
MORIARTY, PATRICK JAMES., Florida Atlantic University, Stronge, William B., College of Business, Department of Economics
Abstract/Description
This thesis is a study of time series modeling techniques applied to the relationship between the rate of inflation and unemployment. The data used in this study are quarterly for the United States from 1948 - 1981. The study begins by reviewing the major theories of inflation and unemployment. Univariate stochastic time series methods are introduced and applied to the above-mentioned relationship. Multivariate stochastic time series methods are then fit to a series of related variables to...
Show moreThis thesis is a study of time series modeling techniques applied to the relationship between the rate of inflation and unemployment. The data used in this study are quarterly for the United States from 1948 - 1981. The study begins by reviewing the major theories of inflation and unemployment. Univariate stochastic time series methods are introduced and applied to the above-mentioned relationship. Multivariate stochastic time series methods are then fit to a series of related variables to investigate the validity of the lag structures employed on the relationship between inflation and unemployment.
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Date Issued
1982
PURL
http://purl.flvc.org/fcla/dt/14114
Subject Headings
Unemployment--United States--Effect of inflation on, Time-series analysis
Format
Document (PDF)
TIME SERIES ANALYSIS OF INCOME AND CONSUMPTION
Title
TIME SERIES ANALYSIS OF INCOME AND CONSUMPTION.
Creator
THEALL, GEORGE ALBERT., Florida Atlantic University, Stronge, William B., College of Business, Department of Economics
Abstract/Description
This thesis uses time series analysis to construct models of income and consumption in the United States between 1947 and 1983. The data are quarterly observations on three measures of income and two of consumption. The study begins with a survey of univariate and multivariate model building techniques. With the life cycle - permanent income hypothesis as a foundation, theoretical models of income and consumption are discussed. These models are then fit to the data and examined. Tests for...
Show moreThis thesis uses time series analysis to construct models of income and consumption in the United States between 1947 and 1983. The data are quarterly observations on three measures of income and two of consumption. The study begins with a survey of univariate and multivariate model building techniques. With the life cycle - permanent income hypothesis as a foundation, theoretical models of income and consumption are discussed. These models are then fit to the data and examined. Tests for causality are also covered in order to determine the manner in which the two processes are related in a multivariate model.
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Date Issued
1983
PURL
http://purl.flvc.org/fcla/dt/14184
Subject Headings
Income--United States, Consumption (Economics)--United States, Time-series analysis
Format
Document (PDF)
intelligent neural network forecaster to predict the Standard & Poor 500's index
Title
An intelligent neural network forecaster to predict the Standard & Poor 500's index.
Creator
Shah, Sulay Bipin., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
In this thesis we present an intelligent forecaster based on neural network technology to capture the future path of the market indicator. This thesis is about the development of a new methodology in financial forecasting. An effort is made to develop a neural network forecaster using the financial indicators as the input variables. A complex recurrent neural network is used to capture the behavior of the nonlinear characteristics of the S&P 500. The main outcome of this research is, a...
Show moreIn this thesis we present an intelligent forecaster based on neural network technology to capture the future path of the market indicator. This thesis is about the development of a new methodology in financial forecasting. An effort is made to develop a neural network forecaster using the financial indicators as the input variables. A complex recurrent neural network is used to capture the behavior of the nonlinear characteristics of the S&P 500. The main outcome of this research is, a systematic way of constructing a forecaster for nonlinear and non-stationary data series of S&P 500 that leads to very good out-of-sample prediction. The results of the training and testing of the network are presented along with conclusion. The tool used for the validation of this research is "Brainmaker". This thesis also contains a brief survey of available tools for financial forecasting.
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Date Issued
1999
PURL
http://purl.flvc.org/fcla/dt/15741
Subject Headings
Neural networks (Computer science), Stock price forecasting, Time-series analysis
Format
Document (PDF)
intelligent GMDH forecaster for forecasting certain variables in financial markets
Title
An intelligent GMDH forecaster for forecasting certain variables in financial markets.
Creator
Mehta, Sandeep., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
In this thesis, application of GMDH Algorithm to real life problems is studied. A particular type of GMDH Algorithm namely TMNN is chosen for this purpose. An effort is made to forecast S&P Index Closing Value with the help of the forecaster. The performance of the TMNN Algorithm is simulated by implementing a tool in C++ for developing forecast models. The validation of this simulation tool is carried out with Sine Wave Values and performance analysis is done in a noisy environment. The...
Show moreIn this thesis, application of GMDH Algorithm to real life problems is studied. A particular type of GMDH Algorithm namely TMNN is chosen for this purpose. An effort is made to forecast S&P Index Closing Value with the help of the forecaster. The performance of the TMNN Algorithm is simulated by implementing a tool in C++ for developing forecast models. The validation of this simulation tool is carried out with Sine Wave Values and performance analysis is done in a noisy environment. The noisy environment tests the TMNN forecaster for its robustness. The primary goal of this research is to develop a simulation software based on TMNN Algorithm for forecasting stock market index values. The main inputs are previous day's closing values and the output is predicted closing index.
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Date Issued
2002
PURL
http://purl.flvc.org/fcla/dt/12996
Subject Headings
GMDH algorithms, Neural networks (Computer science), Time-series analysis, Pattern recognition systems
Format
Document (PDF)
new methodology to predict certain characteristics of stock market using time-series phenomena
Title
A new methodology to predict certain characteristics of stock market using time-series phenomena.
Creator
Shah, Trupti U., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
The goal of time series forecasting is to identify the underlying pattern and use these patterns to predict the future path of the series. To capture the future path of a dynamic stock market variable is one of the toughest challenges. This thesis is about the development of a new methodology in financial forecasting. An effort is made to develop a neural network forecaster using time-series phenomena. The main outcome of this new approach for financial forecasting is a systematic way of...
Show moreThe goal of time series forecasting is to identify the underlying pattern and use these patterns to predict the future path of the series. To capture the future path of a dynamic stock market variable is one of the toughest challenges. This thesis is about the development of a new methodology in financial forecasting. An effort is made to develop a neural network forecaster using time-series phenomena. The main outcome of this new approach for financial forecasting is a systematic way of constructing a Neural Network Forecaster for nonlinear and non-stationary time-series data that leads to very good out-of-sample prediction. The tool used for the validation of this research is "Brainmaker". This thesis also contains a small survey of available tools used for financial forecasting.
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Date Issued
1999
PURL
http://purl.flvc.org/fcla/dt/15677
Subject Headings
Time-series analysis, Neural networks (Computer science), Stock price forecasting
Format
Document (PDF)
Time series analysis and correlation dimension estimation: Mathematical foundation and applications
Title
Time series analysis and correlation dimension estimation: Mathematical foundation and applications.
Creator
Jiang, Wangye, Florida Atlantic University, Ding, Mingzhou, Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description
A time series is a data set of a single quantity sampled at intervals T time units apart. It is widely used to represent a chaotic dynamical system. The correlation dimension measures the complexity of a dynamical system. Using the delay-coordinate map and the extended GP algorithm one can estimate the correlation dimension of an experimental dynamical system from measured time series. This thesis discusses the mathematical foundation of the methods and the corresponding applications. The...
Show moreA time series is a data set of a single quantity sampled at intervals T time units apart. It is widely used to represent a chaotic dynamical system. The correlation dimension measures the complexity of a dynamical system. Using the delay-coordinate map and the extended GP algorithm one can estimate the correlation dimension of an experimental dynamical system from measured time series. This thesis discusses the mathematical foundation of the methods and the corresponding applications. The embedding theorems and their relationship with dimension preservation are reviewed in detail, but more attention is focussed on the concept development.
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Date Issued
1995
PURL
http://purl.flvc.org/fcla/dt/15213
Subject Headings
Time-series analysis--Mathematical models, Chaotic behavior in systems
Format
Document (PDF)
FINANCIAL TIME-SERIES ANALYSIS WITH DEEP NEURAL NETWORKS
Title
FINANCIAL TIME-SERIES ANALYSIS WITH DEEP NEURAL NETWORKS.
Creator
Rimal, Binod, Hahn, William Edward, Florida Atlantic University, Department of Mathematical Sciences, Charles E. Schmidt College of Science
Abstract/Description
Financial time-series data are noisy, volatile, and nonlinear. The classic statistical linear models may not capture those underlying structures of the data. The rapid advancement in artificial intelligence and machine learning techniques, availability of large-scale data, and increased computational capabilities of a machine opens the door to developing sophisticated deep learning models to capture the nonlinearity and hidden information in the data. Creating a robust model by unlocking the...
Show moreFinancial time-series data are noisy, volatile, and nonlinear. The classic statistical linear models may not capture those underlying structures of the data. The rapid advancement in artificial intelligence and machine learning techniques, availability of large-scale data, and increased computational capabilities of a machine opens the door to developing sophisticated deep learning models to capture the nonlinearity and hidden information in the data. Creating a robust model by unlocking the power of a deep neural network and using real-time data is essential in this tech era. This study constructs a new computational framework to uncover the information in the financial time-series data and better inform the related parties. It carries out the comparative analysis of the performance of the deep learning models on stock price prediction with a well-balanced set of factors from fundamental data, macroeconomic data, and technical indicators responsible for stock price movement. We further build a novel computational framework through a merger of recurrent neural networks and random compression for the time-series analysis. The performance of the model is tested on a benchmark anomaly time-series dataset. This new computational framework in a compressed paradigm leads to improved computational efficiency and data privacy. Finally, this study develops a custom trading simulator and an agent-based hybrid model by combining gradient and gradient-free optimization methods. In particular, we explore the use of simulated annealing with stochastic gradient descent. The model trains a population of agents to predict appropriate trading behaviors such as buy, hold, or sell by optimizing the portfolio returns. Experimental results on S&P 500 index show that the proposed model outperforms the baseline models.
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Date Issued
2022
PURL
http://purl.flvc.org/fau/fd/FA00014009
Subject Headings
Neural networks (Computer science), Deep learning (Machine learning), Time-series analysis, Stocks, Simulated annealing (Mathematics)
Format
Document (PDF)
An investigation of summer upwelling across central Florida's Atlantic coast: The case for wind stress forcing
Title
An investigation of summer upwelling across central Florida's Atlantic coast: The case for wind stress forcing.
Creator
Pitts, Patrick A., Smith, Ned P.
Date Issued
1997
PURL
http://purl.flvc.org/FCLA/DT/3172959
Subject Headings
Upwelling (Oceanography), Ocean-atmosphere interaction, Atlantic Coast (Fla.) --Environmental conditions, Ocean currents --Atlantic Ocean, Time-series analysis
Format
Document (PDF)