Global ETD Search

1	None Yen, Chia-Hsin 09 July 2006 (has links) ¡@¡@The purpose of this research is to employ the STAR model in discussing and analyzing the relationship between stock index and macroeconomic variables in Taiwan, Japan and Korea. ¡@¡@Monthly stock market index data is analyzed over the period January 1990 to December 2000, with the sample period from January 2001 to April 2005 being used in an out-of -sample forecasting exercise. The macroeconomic variables considered in this paper include money supply, consumer price index, industrial production index, interest rate and exchange rate. ¡@¡@The empirical results of Taiwan, Japan and Korea show that LSTAR & ESTAR model improve both the in-sample fit and out-of-sample forecast of the data over both the linear model alternative. BDS Smooth Transition Autoregressive model Root Mean Squared Error Ljung-Box Q
2	Proactive university library book recommender system Mekonnen, Tadesse Zewdu January 2021 (has links) M. Tech. (Department of Information Communication Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / Too many options on the internet are the reason for the information overload problem to obtain relevant information. A recommender system is a technique that filters information from large sets of data and recommends the most relevant ones based on people‟s preferences. Collaborative and content-based techniques are the core techniques used to implement a recommender system. A combined use of both collaborative and content-based techniques called hybrid techniques provide relatively good recommendations by avoiding common problems arising from each technique. In this research, a proactive University Library Book Recommender System has been proposed in which hybrid filtering is used for enhanced and more accurate recommendations. The prototype designed was able to recommend the highest ten books for each user. We evaluated the accuracy of the results using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). A measure value of 0.84904 MAE and 0.9579 RMSE found by our system shows that the combined use of both techniques gives an improved prediction accuracy for the University Library Book Recommender System. Hybrid techniques Hybrid filtering Mean Absolute Error (MAE) Root Mean Squared Error (RMSE) Dissertations, Academic -- South Africa Electronic data processing Self-organizing systems
3	Employing Bayesian Vector Auto-Regression (BVAR) method as an altenative technique for forecsating tax revenue in South Africa Molapo, Mojalefa Aubrey 11 1900 (has links) Statistics / M. Sc. (Statistics) Corporate income tax Personal income tax Total tax revenue Forecasting Bayesian Vector Autoregressive (BVAR) Error Trend Seasonal (ETS) Autoregressive moving Average (ARMA) Root mean squared error (RMSE) Autocorrelation 519.542 Bayesian statistical decision theory Vector automobile Tax revenue estimating Corporations -- Taxation Income tax Standard deviations

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