Throughout history, there has been a great emphasis placed on the ability to predict future events. The value of such prognostication varies between situations and domains, but the objective remains the same. Is it possible to use current or past observations to forecast future events? One specific area in which such insight is sought after is the technology industry. Forecasting technological changes provides direction to many individuals, businesses, and governments. There are many different approaches to creating technology forecasting models. This is further complicated by the vast amount of data that could be used to develop such models. One potential source of data exists in the form of patents. Patents can be viewed as benchmarks and indicators of technological progression. By examining past patent trends, it may be possible to discover new technological advances, foci of innovation, or effects on econometric variables. While there is a suitable volume of time series forecasting models available to accomplish univariate predictions, there seems to be limited studies involving the assessment of multivariate time series trends. This research will demonstrate that it is possible to discover relationships between technological changes and rates of employment. This will be accomplished by comparing clustered US patent trends with the trends of US job openings over a given period of time. The patent clustering will be carried out using K-Means Clustering and the comparison of trends will be done using the Extended Frobenius Entropy Measurement (EFrEM) technique. The entire process will be referred to as Technology Forecasting for Employment Growth (TFEG). [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]