Engineering Commons^™

Forecasting Economic Growth And Movements With Wavelet Transform And Arima Model, Omar Alsinglawi, Omar Alsinglawi, Mohammad Aladwan, Mohammad Aladwan, Saddam Alwadi, Saddam Alwadi

Applied Mathematics & Information Sciences

This study uses historical data and modern statistical models to forecast future Gross Domestic Product (GDP) in Jordan. The Wavelet Transformation model (WT) and Autoregressive Integrated Moving Average (ARIMA) model were applied to the time series data and yielded a best-fitting result of (2,1,1) for estimating GDP between 2022-2031. The study concludes that GDP is expected to increase with a positive growth rate of around 3.22%, and recommends government agencies to monitor GDP, strengthen existing policies, and adopt necessary economic reforms to support growth. Additionally, the private sector is encouraged to enhance production tools to achieve economic growth that benefits …

A Hydropower Facility As An Energy Water Signal Processor, Asha Shibu

Doctoral Dissertations

In recent times, various efforts have been made to address the challenge of adequately representing hydropower systems in modeling frameworks, accounting for the lack of data to represent the multiple constraints in hydropower operation. This research is a pilot data-driven methodology for characterizing, classifying, and comparing the water-to-energy and energy-to-water signal transformations that hydropower facilities as signal processors accomplish. In this study, a Box Jenkins transfer function/noise model is used to identify the relationship between reservoir inflows and outflows. For examining the feasibility of this methodology, 5-minute fleet data for five storage and five run-of-river facilities was provided by the …

Runtime Power Allocation Based On Multi-Gpu Utilization In Gamess, Masha Sosonkina, Vaibhav Sundriyal, Jorge Luis Galvez Vallejo

Electrical & Computer Engineering Faculty Publications

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize performance under a given power budget by distributing the available power according to the relative GPU utilization. Time series forecasting methods were used to develop workload prediction models that provide accurate prediction of GPU utilization during application execution. Experiments were performed on a multi-GPU computing platform DGX-1 equipped with eight NVIDIA V100 GPUs used for quantum chemistry calculations in the GAMESS package. For a limited power budget, the proposed strategy …

A Deep Learning Model For Predicting Covid-19 Transmission In Connecticut, Nathan Choi

Honors Scholar Theses

COVID-19 has immensely impacted life as we know it, as the virus quickly spread throughout the entire world in a matter of weeks since its emergence. It has toppled economies, tested healthcare systems worldwide, and has un- fortunately taken the lives of many in the process. While extensive research has analyzed the issue on a large scale, focusing on entire countries and states, there has not been as much focus on the meso-scale, mainly compris- ing towns and cities, due to the lack of available COVID-19 data at this scale. However, in the case of countries like the United States …

Sustainable Agricultural Development In The Western Desert Of Egypt Under Climate Change: A Case Study Of The Siwa Region, Noha Hossam Moghazy

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The Siwa region located in the Western Desert of Egypt is a natural depression and has a large volume of groundwater from the non-renewable Nubian Sandstone Aquifer System (NSAS). Recently the government initiated a development project to reclaim 1.5 million acres where most of the lands are located in the Western Desert to use available groundwater from NSAS. The primary goal of this project is to increase agricultural areas enabling rural development. Siwa is one of the areas that will be reclaimed in the desert by about 30,000 acres consisting of good soil quality. This dissertation aims to understand the …

Price Optimization For Revenue Maximization At Scale, Nikhil Gupta, Massimiliano Moro, Kailey A. Ayala, Bivin Sadler

SMU Data Science Review

This study presents a novel approach to price optimization in order to maximize revenue for the distribution market of non-perishable products. Data analysis techniques such as association mining, statistical modeling, machine learning, and an automated machine learning platform are used to forecast the demand for products considering the impact of pricing. The techniques used allow for accurate modeling of the customer’s buying patterns including cross effects such as cannibalization and the halo effect. This study uses data from 2013 to 2019 for Super Premium Whiskey from a large distributor of alcoholic beverages. The expected demand and the ideal pricing strategy …

Brexit Election: Forecasting A Conservative Party Victory Through The Pound Using Arima And Facebook's Prophet, James Usher, Pierpaolo Dondio

Conference papers

On the 30th October, 2019, the markets watched as British Prime Minister, Boris Johnson, took a massive political gamble to call a general election to break the Withdrawal Agreement stalemate in the House of Commons to “Get BREXIT Done”. The pound had been politically sensitive owing to BREXIT uncertainty. With the polls indicating a Conservative win on 4thDecember, 2019, the margin of victory could be observed through increases in the pound. The outcome of a Conservative party victory would benefit the pound by removing the current market turbulence. We look to provide a short-term forecast of the pound. Our approach …

Crude Oil Price Prediction With Decision Tree Based Regression Approach, Engu Chen, Xin James He

Journal of International Technology and Information Management

Crude oil is an essential commodity for industry and the prediction of its price is crucial for many business entities and government organizations. While there have been quite a few conventional statistical models to forecast oil prices, we find that there is not much research using decision tree models to predict crude oil prices. In this research, we develop decision tree models to forecast crude oil prices. In addition to historical crude oil price time series data, we also use some predictor variables that would potentially affect crude oil prices, including crude oil demand and supply, and monthly GDP and …

Modeling Of Grace-Derived Groundwater Information In The Colorado River Basin, Md Mafuzur Rahaman, Balbhadra Thakur, Ajay Kalra, Sajjad Ahmad

Civil and Environmental Engineering and Construction Faculty Research

Groundwater depletion has been one of the major challenges in recent years. Analysis of groundwater levels can be beneficial for groundwater management. The National Aeronautics and Space Administration’s twin satellite, Gravity Recovery and Climate Experiment (GRACE), serves in monitoring terrestrial water storage. Increasing freshwater demand amidst recent drought (2000–2014) posed a significant groundwater level decline within the Colorado River Basin (CRB). In the current study, a non-parametric technique was utilized to analyze historical groundwater variability. Additionally, a stochastic Autoregressive Integrated Moving Average (ARIMA) model was developed and tested to forecast the GRACE-derived groundwater anomalies within the CRB. The ARIMA model …

Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater

SMU Data Science Review

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best …

Forecasting Anomalous Events And Performance Correlation Analysis In Event Data, Sonya Leech [Thesis]

Dissertations

Classical and Deep Learning methods are quite common approaches for anomaly detection. Extensive research has been conducted on single point anomalies. Collective anomalies that occur over a set of two or more durations are less likely to happen by chance than that of a single point anomaly. Being able to observe and predict these anomalous events may reduce the risk of a server’s performance. This paper presents a comparative analysis into time-series forecasting of collective anomalous events using two procedures. One is a classical SARIMA model and the other is a deep learning Long-Short Term Memory (LSTM) model. It then …

Understanding And Modeling Taxi Demand Using Time Series Models, Sabiheh Faghih

Dissertations and Theses

The spatio-temporal variations in demand for transportation, particularly taxis, are impacted by various factors such as commuting, weather, road work and closures, disruption in transit services, etc. Identifying the factors that influence taxi demand and understanding its dynamic provide planners with the information necessary to improve the transportation systems and also help drivers to reduce their vacant time.

This dissertation focuses on important factors affecting the demand. In the beginning, the impact of price changes on the demand is studied. Chapter One discusses how the seasonal effects and trends are removed from the demand, and then price elasticity for demand …

Crude Oil Prices Forecasting: Time Series Vs. Svr Models, Xin James He

Journal of International Technology and Information Management

This research explores the weekly crude oil price data from U.S. Energy Information Administration over the time period 2009 - 2017 to test the forecasting accuracy by comparing time series models such as simple exponential smoothing (SES), moving average (MA), and autoregressive integrated moving average (ARIMA) against machine learning support vector regression (SVR) models. The main purpose of this research is to determine which model provides the best forecasting results for crude oil prices in light of the importance of crude oil price forecasting and its implications to the economy. While SVR is often considered the best forecasting model in …

Combination Forecasting Of Stock Index Time Series Based On Cooperative Game Theory, Luo Wei

Journal of System Simulation

Abstract: In view of the characteristics of nonlinear, large amplitude, frequent fluctuations in China's stock market, a prediction method of intelligent composite stock index time series based on the cooperative game is presented. The prediction model of stock index time series is established by using neural network method based on the correlations among the various economic indicators, and the development trend and laws of stock index time series are established by using the improved ARIMA method. The two methods are combined by importing cooperative game method. Simulation results show that the prediction accuracy of the presented method is controlled …

An Application Of Box-Jenkins Approach In Forecasting Nigerian Crude Oil Prices., Bashir Umar Faruk

Bullion

Nigerian government has been adopting Moving Average (MA) method in pegging crude oil price benchmark. However, large discrepancy between the projected oil price benchmark and the actual international crude oil prices is observed over time. Therefore, the main objective of this research is to investigate whether Box-Jenkins approach could provide a lasting solution to the problem of inefficient oil price forecast in the Nigerian budgeting process. In our quest for an appropriate oil benchmark, monthly bonny light crude oil prices for the period of April 1986 to December 2015 are used.

Geospatial Analysis Of The Global Supply Chain And Transportation Infrastructure Considering Extreme Weather, Climate, And Sustainable Energy Policies, Craig Freeman Davis

Electronic Theses and Dissertations

The basis of this research is an investigation into the demand, costs, and emissions of a container freight shipping route from South America to the Port of Charleston, South Carolina. Ports and shipping routes play the most crucial role in the global supply chain, allowing people to maintain their standard of living. Once ashore, the delivery routes to five major metropolitan market cities were optimized for the lowest shipping costs for road and freight rail. The costs of transportation are a major factor in the ultimate price of consumer goods and thus must be minimized in the transportation process. Increasing …

Forecasting The Workload With A Hybrid Model To Reduce The Inefficiency Cost, Xinwei Pan

Theses and Dissertations--Mechanical Engineering

Time series forecasting and modeling are challenging problems during the past decades, because of its plenty of properties and underlying correlated relationships. As a result, researchers proposed a lot of models to deal with the time series. However, the proposed models such as Autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs) only describe part of the properties of time series. In this thesis, we introduce a new hybrid model integrated filter structure to improve the prediction accuracy. Case studies with real data from University of Kentucky HealthCare are carried out to examine the superiority of our model. Also, …

An Exploration Of The Relationship Between The Partisan-Business Cycle And Economic Inequality Within Developed Economies, Richard O'Doherty

Dissertations

Recent contributions to the study of inequality have provided strong evidence towards the presence of an established trend, over several decades, of growing economic inequality (with a particular focus on distribution within their tails; i.e. top 10%, 1%) across countries with developed economies and indications of similar trends across developing economies. While the causality and influencing factors to these trends has widely been discussed, and has range from declining domestic growth rates as economies move towards high mass consumption states to globalisation, political decision making and policy application been referred to as both contributory or an instrument for dampening such …

Analysis And Management Of The Price Volatility In The Construction Industry, Alireza Joukar

LSU Doctoral Dissertations

The problem of price volatility as it pertains to material and labor is a major source of risk and financial distress for all the participants in the construction industry. The overarching goal of this dissertation is to address this problem from both viewpoints of risk analysis and risk management. This dissertation offers three independent papers addressing this goal. In the first paper using the Engineering News Record Construction Cost Index (ENR CCI), a predictive model is developed. The model uses General Autoregressive Conditional Heteroscedastic (GARCH) approach which facilitates both forecasting of the future values of the CCI, and capturing and …

A Gasoline Demand Model For The United States Light Vehicle Fleet, Diana Rey

Electronic Theses and Dissertations

The United States is the world's largest oil consumer demanding about twenty five percent of the total world oil production. Whenever there are difficulties to supply the increasing quantities of oil demanded by the market, the price of oil escalates leading to what is known as oil price spikes or oil price shocks. The last oil price shock which was the longest sustained oil price run up in history, began its course in year 2004, and ended in 2008. This last oil price shock initiated recognizable changes in transportation dynamics: transit operators realized that commuters switched to transit as a …