Matching Items (3)
Description
Data mining is increasing in importance in solving a variety of industry problems. Our initiative involves the estimation of resource requirements by skill set for future projects by mining and analyzing actual resource consumption data from past projects in the semiconductor industry. To achieve this goal we face difficulties like data with relevant consumption information but stored in different format and insufficient data about project attributes to interpret consumption data. Our first goal is to clean the historical data and organize it into meaningful structures for analysis. Once the preprocessing on data is completed, different data mining techniques like clustering is applied to find projects which involve resources of similar skillsets and which involve similar complexities and size. This results in "resource utilization templates" for groups of related projects from a resource consumption perspective. Then project characteristics are identified which generate this diversity in headcounts and skillsets. These characteristics are not currently contained in the data base and are elicited from the managers of historical projects. This represents an opportunity to improve the usefulness of the data collection system for the future. The ultimate goal is to match the product technical features with the resource requirement for projects in the past as a model to forecast resource requirements by skill set for future projects. The forecasting model is developed using linear regression with cross validation of the training data as the past project execution are relatively few in number. Acceptable levels of forecast accuracy are achieved relative to human experts' results and the tool is applied to forecast some future projects' resource demand.
ContributorsBhattacharya, Indrani (Author) / Sen, Arunabha (Thesis advisor) / Kempf, Karl G. (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis serves as an experimental investigation into the potential of machine learning through attempting to predict the future price of a cryptocurrency. Through the use of web scraping, short interval data was collected on both Bitcoin and Dogecoin. Dogecoin was the dataset that was eventually used in this thesis due to its relative stability compared to Bitcoin. At the time of the data collection, Bitcoin became a much more frequent topic in the media and had more significant fluctuations due to it. The data was processed into consistent three separate, consistent timesteps, and used to generate predictive models. The models were able to accurately predict test data given all the preceding test data but were unable to autoregressively predict future data given only the first set of test data points. Ultimately, this project helps illustrate the complexities of extended future price prediction when using simple models like linear regression.
ContributorsMurwin, Andrew (Author) / Bryan, Chris (Thesis director) / Ghayekhloo, Samira (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-12
Description
This paper quantitatively analyses the relation between the return of private
seasoned equity offerings and variables of market and firm characteristics in China Ashare
market. A multiple-factor linear regression model is constructed to estimate this
relation and the result canhelp investors to determine the future return of private
placement stocks.
In this paper, I first review past theories about private placement stocks, including how
the large shareholder participation, the discount of private offerings, the firm
characteristics, and the investment on firm value will affect the return of private
offerings.
According to the past literature, I propose four main factors that may affect the
return of private placement. They are the large shareholders participation in private
placement; the discount that private placement could offer; the characteristics of the
companies that offer a private placement and the intrinsic value of such companies. I
adopt statistic and correlational analysis to test the impact of each factor. Then,
according to this single-factor analysis, I set up a multiple-factor linear regression model
on private seasoned equity offerings return in Chapter Four.
In the last two chapters, I apply this quantitative model to other fields. I use this
model to testify current financial products of private placement and develop investmen
strategies on stocks with private seasoned equity offerings in secondary market. My
quantitative strategy is useful according to the result of setback test.
seasoned equity offerings and variables of market and firm characteristics in China Ashare
market. A multiple-factor linear regression model is constructed to estimate this
relation and the result canhelp investors to determine the future return of private
placement stocks.
In this paper, I first review past theories about private placement stocks, including how
the large shareholder participation, the discount of private offerings, the firm
characteristics, and the investment on firm value will affect the return of private
offerings.
According to the past literature, I propose four main factors that may affect the
return of private placement. They are the large shareholders participation in private
placement; the discount that private placement could offer; the characteristics of the
companies that offer a private placement and the intrinsic value of such companies. I
adopt statistic and correlational analysis to test the impact of each factor. Then,
according to this single-factor analysis, I set up a multiple-factor linear regression model
on private seasoned equity offerings return in Chapter Four.
In the last two chapters, I apply this quantitative model to other fields. I use this
model to testify current financial products of private placement and develop investmen
strategies on stocks with private seasoned equity offerings in secondary market. My
quantitative strategy is useful according to the result of setback test.
ContributorsCao, Xuan (Author) / Pei, Ker-Wei (Thesis advisor) / Li, Feng (Thesis advisor) / Qian, Jun (Committee member) / Arizona State University (Publisher)
Created2017