Voluntary turnover has historically been a problem for today's organizations. Traditional models of turnover continue to be utilized in a number of ways in both academia and industry. A newer model of turnover, job embeddedness, has recently been developed in an attempt to better predict voluntary turnover than existing models. Job embeddedness consists of organizational fit, organizational sacrifice, and organizational links. The purpose of this study is to two fold. First, psychometric analyses were conducted on the job embeddedness model. Exploratory factor analyses were conducted on the dimensions of job embeddedness, which revealed a combined model consisting of five factors. This structure was then analyzed using confirmatory factor analysis, assessing a 1, 3, and 5 factor model structure. The confirmatory factor analysis established the use of the 5 factor model structure in subsequent analysis in this study. The second purpose of this study is to compare the predictive power of the job embeddedness model versus that of the traditional models of turnover. The traditional model of turnover is comprised of job satisfaction, organizational commitment, and perceived job alternatives. In order to compare the predictive power of the job embeddedness and traditional model of voluntary turnover, a series of structural equation model analyses were conducting using LISREL. The job embeddedness model, alone, was found to be the best fit with the sample data. This fit was improved over the other two models tested (traditional model and the combination of the traditional and job embeddedness model). In addition to assessing which model better predicts voluntary turnover, it was tested which age group and gender is a better fit with the job embeddedness model. It was found that the job embeddedness model better predicts turnover intention for older respondents and males.

In the last decade, deep learning models have been successfully applied to a variety of applications and solved many tasks. The ultimate goal of this study is to produce deep learning models to improve the skills of forecasting ocean dynamic events in general and those of the Loop Current (LC) system in particular. A specific forecast target is to predict the geographic location of the (LC) extension and duration, LC eddy shedding events for a long lead time with high accuracy. Also, this study aims to improve the predictability of velocity fields (or more precisely, velocity volumes) of subsurface currents. In this dissertation, several deep learning based prediction models have been proposed. The core of these models is the Long-Short Term Memory (LSTM) network. This type of recurrent neural network is trained with Sea Surface Height (SSH) and LC velocity datasets. The hyperparameters of these models are tuned according to each model's characteristics and data complexity. Prior to training, SSH and velocity data are decomposed into their temporal and spatial counterparts.A model uses the Robust Principle Component Analysis is first proposed, which produces a six-week lead time in forecasting SSH evolution. Next, the Wavelet+EOF+LSTM (WELL) model is proposed to improve the forecasting capability of a prediction model. This model is tested on the prediction of two LC eddies, namely eddy Cameron and Darwin. It is shown that the WELL model can predict the separation of both eddies 10 and 14 weeks ahead respectively, which is two more weeks than the DAC model. Furthermore, the WELL model overcomes the problem due to the partitioning step involved in the DAC model. For subsurface currents forecasting, a layer partitioning method is proposed to predict the subsurface field of the LC system. A weighted average fusion is used to improve the consistency of the predicted layers of the 3D subsurface velocity field. The main challenge of forecasting of the LC and its eddies is the small number of events that have occurred over time, which is only once or twice a year, which makes the training task difficult. Forecasting the velocity of subsurface currents is equally challenging because of the limited insitu measurements. / Includes bibliography. / Dissertation (PhD)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

The aim of the thesis was to create a tool for time-series prediction based on deep learning. The first part of the work is a brief description of deep learning and its comparison to classical machine learning. In the next section contains brief analysis of some tools, that are already used for time-series forecasting. The last part is focused on the analysis of the problem as well as on the actual creation of the program.

The first line of defense for the threat of an oncoming hurricane are meteorologists. From their guidance, warnings are drafted and evacuation plans are made ready. This study explores uncertainty that operational meteorologists encounter with hurricane prediction, and more importantly, how meteorologists translate the uncertainty for the public. The study is based on a web survey of individual meteorologists, in cooperation with the National Weather Association (NWA). The survey received 254 responses with an estimated 18% response rate. Specifically, the study focuses on three key areas: displaying uncertainty in hurricane track forecasts, perceived relationships between the public and the media and message characteristics on various platforms (e.g., television, web, and social media), and reliance on numerical weather prediction in the forecasting process. Results show that tracking graphics are varied between their use and usefulness and meteorologists think that they have a bigger role in information dissemination than previously thought. / National Science Foundation (NSF) Grant CMMI1520338

Thesis. 1975. M.S.--Massachusetts Institute of Technology. Dept. of Meteorology. / Bibliography: leaves 66-67. / by William K.-F. Grant, Jr. / M.S.

No description available.

This paper develops a series of Early Warning System models for debt crises. This paper uses a Debt Pressure index to define crisis periods and then demonstrates how one can go about trying to forecast these periods using Logit and Markov-switching Models. An alternative approach, whereby ordinary least squares (OLS) is used to create Early Warning System models, is introduced. A graphical analysis is also conducted. Three useful Early Warning System models emerge from this study.

M.P.H., Faculty of Health Sciences, University of the Witwatersrand, 2011

This paper investigates the Baltic Dry Index; an often misunderstood index, which tracks the cost of shipping dry bulk cargo globally. The research is based on the hypothesis that movements in the Baltic Dry Index price are driven largely by changes in the underlying demand for goods which are consumed globally. Accordingly, this paper aims to investigate whether changes in the Baltic Dry Index price may be used to predict future economic movements in a South African context. In this regard, the paper first conducts a thorough synthesis of the available literature, in order to formulate the conclusion that the Baltic Dry Index price is driven by a multitude of variables, including the global demand for goods, the global supply of ships, the laycan period, bunker prices, global piracy, global winter severity, as well as the inclusion of a cyclical component. The global demand for goods is concluded to be chief among these. Based on these findings, the paper then conducts empirical testing on the usefulness of the BDI in a South African context, and concludes that the Baltic Dry Index is useful when used as a leading economic indicator in South African, especially when used in order to predict long-term economic movements, across a period of 3 – 4.5 years. Finally, strong evidence is found to support the existence of a relationship between the BDI and the Johannesburg Stock Exchange Mining Index, although further investigation is required in order to form a definitive conclusion in this regard.

Credit has become very important in the global economy (Cynamon and Fazzari, 2008). The Altman (1968) failure prediction model, or derivatives thereof, are often used in the identification and selection of financially distressed companies as it is recognized as one of the most reliable in predicting company failure (Eidleman, 1995). Failure of a firm can cause substantial losses to creditors and shareholders, therefore it is important, to detect company failure as early as possible. This research report empirically tests the Altman (1968) failure prediction model on 227 South African JSE listed companies using data from the 2008 financial year to calculate the Z-score within the model, and measuring success or failure of firms in the 2009 and 2010 years. The results indicate that the Altman (1968) model is a viable tool in predicting company failure for firms with positive Z-scores, and where Z-scores do not fall into the range of uncertainty as specified. The results also suggest that the model is not reliable when the Z–scores are negative or when they are in the range of uncertainty (between 2.99 and 1.81). If one is able to predict firm failure in advance, it should be possible for management to take steps to avert such an occurrence (Deakin, 1972; Keasey and Watson, 1991; Platt and Platt, 2002).