Call number: LD2668 .T4 1957 R58 / Master of Science

Understanding the effects of quality metrics on the user behavior is important for the increasing number of content providers in order to maintain a competitive edge. The two data sets used are gathered from a provider of live streaming and a provider of video on demand streaming. The important quality and non quality features are determined by using both correlation metrics and relative importance determined by machine learning methods. A model that can predict and simulate the user behavior is developed and tested. A time series model, machine learning model and a combination of both are compared. Results indicate that both quality features and non quality features are important in understanding user behavior, and the importance of quality features are reduced over time. For short prediction times the model using quality features is performing slightly better than the model not using quality features.

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

Ying-Qian Zhang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 115-124). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Objective --- p.2 / Chapter 1.3 --- Contributions --- p.3 / Chapter 1.4 --- Thesis Overview --- p.4 / Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Takens' Theorem --- p.6 / Chapter 2.2 --- Linear Models for Prediction --- p.7 / Chapter 2.2.1 --- Autoregressive Model --- p.7 / Chapter 2.2.2 --- Moving Average Model --- p.8 / Chapter 2.2.3 --- Autoregressive-moving Average Model --- p.9 / Chapter 2.2.4 --- Fitting a Linear Model to a Given Time Series --- p.9 / Chapter 2.2.5 --- State-space Reconstruction --- p.10 / Chapter 2.3 --- Neural Network Models for Time Series Processing --- p.11 / Chapter 2.3.1 --- Feed-forward Neural Networks --- p.11 / Chapter 2.3.2 --- Recurrent Neural Networks --- p.14 / Chapter 2.3.3 --- Training Algorithms for Recurrent Networks --- p.18 / Chapter 2.4 --- Combining Neural Networks and other approximation techniques --- p.22 / Chapter 3 --- ForeNet: Model and Representation --- p.24 / Chapter 3.1 --- Fourier Recursive Prediction Equation --- p.24 / Chapter 3.1.1 --- Fourier Analysis of Time Series --- p.25 / Chapter 3.1.2 --- Recursive Form --- p.25 / Chapter 3.2 --- Fourier Recurrent Neural Network Model (ForeNet) --- p.27 / Chapter 3.2.1 --- Neural Networks Representation --- p.28 / Chapter 3.2.2 --- Architecture of ForeNet --- p.29 / Chapter 4 --- ForeNet: Implementation --- p.32 / Chapter 4.1 --- Improvement on ForeNet --- p.33 / Chapter 4.1.1 --- Number of Hidden Neurons --- p.33 / Chapter 4.1.2 --- Real-valued Outputs --- p.34 / Chapter 4.2 --- Parameters Initialization --- p.37 / Chapter 4.3 --- Application of ForeNet: the Process of Time Series Prediction --- p.38 / Chapter 4.4 --- Some Implications --- p.39 / Chapter 5 --- ForeNet: Initialization --- p.40 / Chapter 5.1 --- Unfolded Form of ForeNet --- p.40 / Chapter 5.2 --- Coefficients Analysis --- p.43 / Chapter 5.2.1 --- "Analysis of the Coefficients Set, vn " --- p.43 / Chapter 5.2.2 --- "Analysis of the Coefficients Set, μn(d) " --- p.44 / Chapter 5.3 --- Experiments of ForeNet Initialization --- p.47 / Chapter 5.3.1 --- Objective and Experiment Setting --- p.47 / Chapter 5.3.2 --- Prediction of Sunspot Series --- p.49 / Chapter 5.3.3 --- Prediction of Mackey-Glass Series --- p.53 / Chapter 5.3.4 --- Prediction of Laser Data --- p.56 / Chapter 5.3.5 --- Three More Series --- p.59 / Chapter 5.4 --- Some Implications on the Proposed Initialization Method --- p.63 / Chapter 6 --- ForeNet: Learning Algorithms --- p.67 / Chapter 6.1 --- Complex Real Time Recurrent Learning (CRTRL) --- p.68 / Chapter 6.2 --- Batch-mode Learning --- p.70 / Chapter 6.3 --- Time Complexity --- p.71 / Chapter 6.4 --- Property Analysis and Experimental Results --- p.72 / Chapter 6.4.1 --- Efficient initialization:compared with random initialization --- p.74 / Chapter 6.4.2 --- Complex-valued network:compared with real-valued net- work --- p.78 / Chapter 6.4.3 --- Simple architecture:compared with ring-structure RNN . --- p.79 / Chapter 6.4.4 --- Linear model: compared with nonlinear ForeNet --- p.80 / Chapter 6.4.5 --- Small number of hidden units --- p.88 / Chapter 6.5 --- Comparison with Some Other Models --- p.89 / Chapter 6.5.1 --- Comparison with AR model --- p.91 / Chapter 6.5.2 --- Comparison with TDNN Networks and FIR Networks . --- p.93 / Chapter 6.5.3 --- Comparison to a few more results --- p.94 / Chapter 6.6 --- Summarization --- p.95 / Chapter 7 --- Learning and Prediction: On-Line Training --- p.98 / Chapter 7.1 --- On-Line Learning Algorithm --- p.98 / Chapter 7.1.1 --- Advantages and Disadvantages --- p.98 / Chapter 7.1.2 --- Training Process --- p.99 / Chapter 7.2 --- Experiments --- p.101 / Chapter 7.3 --- Predicting Stock Time Series --- p.105 / Chapter 8 --- Discussions and Conclusions --- p.109 / Chapter 8.1 --- Limitations of ForeNet --- p.109 / Chapter 8.2 --- Advantages of ForeNet --- p.111 / Chapter 8.3 --- Future Works --- p.112 / Bibliography --- p.115

by Lawrence Chi-Ho Lee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 91-97). / Abstract also in Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Importance of Forecasting --- p.1 / Chapter 1.2 --- Objective --- p.3 / Chapter Chapter 2 --- Holt-Winters Forecasting Procedure --- p.6 / Chapter 2.1 --- Exponential Smoothing and Holt-Winters Method --- p.6 / Chapter 2.2 --- Relationships Between Holt-Winters models and ARIMA Models --- p.13 / Chapter 2.2.1 --- A Steady Model --- p.14 / Chapter 2.2.2 --- A Growth Model --- p.15 / Chapter 2.2.3 --- The Three-Parameter Holt-Winters Model --- p.18 / Chapter 2.3 --- Some Practical Issues --- p.19 / Chapter 2.3.1 --- Normalizing the Seasonal Factors --- p.20 / Chapter 2.3.2 --- Choosing Starting Values --- p.20 / Chapter 2.3.3 --- Choosing the Smoothing Parameters --- p.22 / Chapter Chapter 3 --- Methods of Constructing Simultaneous Prediction Intervals --- p.24 / Chapter 3.1 --- Three Approximation Procedures --- p.25 / Chapter 3.1.1 --- Bonferroni-type Inequality --- p.26 / Chapter 3.1.2 --- Product-type Inequality --- p.28 / Chapter 3.1.3 --- Chi-square-type Inequality --- p.30 / Chapter 3.2 --- The 'Exact' Procedure --- p.31 / Chapter 3.3 --- Summary --- p.32 / Chapter Chapter 4 --- An Illustrative Example --- p.33 / Table 4.1 - 4.7 --- p.47 / Figure 4.1 - 4.5 --- p.55 / Chapter Chapter 5 --- Simulation Study --- p.60 / Chapter 5.1 --- Holt-Winters Forecasting Procedure for Optimal Model --- p.60 / Chapter 5.2 --- Holt-Winters Forecasting Procedure for Some Non-optimal Models --- p.66 / Chapter 5.3 --- A Comparison of Box-Jenkins Method and Holt-Winters Forecasting Procedure --- p.68 / Chapter 5.4 --- Conclusion --- p.74 / Table 5.1-5.10 --- p.75 / Chapter Chapter 6 --- Further Research --- p.82 / APPENDIXES --- p.87 / REFERENCES --- p.91

本文論述了一種全新的快速探測時間序列中結構性突變點的過程。我們應用了一個新的統計量，平均時間常方差，作為樣本協方差結構改變的代理變量。平均時間常方差也可以表現為所有協方差函數的和。吳(2009)提出了一種能夠遞歸計算平均時間常方差估計值的算法並被我們應用在這篇文，其有效更新計算及記憶複雜度均為O(1) 。在這篇文章中，我們研究了平均時間常方差估計值的漸進分佈並設立了一組置信帶(confidence bands) 來監視時間序列是否有突變發生。根據蒙特卡洛模擬，我們發現這種測試方法有很好的統計特性規模(size) 和檢驗力(power)。微陣列數據(Microarray data) 的實例在我們的文章中也進行了展示。我們的算法只在1. 86GHz的處理器中，需要約2秒就能夠檢測長度為10000 的序列。 / This paper proposes a new and fast change point detection procedure for time series. We develop a new proxy for the change in the (sample) covariance structure, the Time Average Variance Constant (TAVC), which could be expressed as the summation of all the auto-covariance functions. Wu (2009)'s algorithm is implemented to compute the estimate of TAVC recursively with an efficient updating computational and memory complexity of O(1). In this article, we study an asymptotic distribution of TAVC estimator and construct condence bands to monitor whether a change happens in a time series. We show the good size and power properties of the procedure based on Monte Carlo Simulation. Illustrations using microarray data are presented. Our algorithm only takes~2s on a single 1.86GHz processor with a sequence of length 10,000. / Detailed summary in vernacular field only. / Jin, Yong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 38-45). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Time Average Variance Constant --- p.6 / Chapter 2.1 --- What is Time Average Variance Constant? --- p.6 / Chapter 2.2 --- Another Look at TAVC --- p.7 / Chapter 2.3 --- Estimation of Time Average Variance Constant --- p.7 / Chapter 2.3.1 --- Algorithm 1: Wu(2009) --- p.8 / Chapter 2.3.2 --- Algorithm 2: Wu(2009) --- p.8 / Chapter 3 --- Behavior of the Sample Auto-Covariance Structure for a Change in Mean --- p.11 / Chapter 3.1 --- Problem Formulation --- p.11 / Chapter 3.2 --- CUSUM Test --- p.12 / Chapter 3.3 --- Behavior of the Sample Auto-Covariance Structure --- p.14 / Chapter 4 --- Change-Point Tests and Algorithms --- p.17 / Chapter 4.1 --- Asymptotic Normality --- p.17 / Chapter 4.2 --- A Change Point Detection Procedure based on TAVC --- p.22 / Chapter 4.2.1 --- Algorithm 3 --- p.23 / Chapter 4.2.2 --- An example: AR(1) Model with p = 0.4 --- p.24 / Chapter 4.2.3 --- Size And Power --- p.26 / Chapter 4.3 --- Array CGH data Example --- p.28 / Chapter 5 --- Conclusion --- p.31 / Chapter 6 --- Appendix --- p.32 / Chapter 6.1 --- Proof of Theorem 3.1 --- p.32 / Bibliography --- p.38

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Lau Yung Hang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 78-82). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / List of Figures --- p.viii / List of Tables --- p.x / List of Algorithms --- p.xi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Justifying the Need for US and DTW --- p.1 / Chapter 1.2 --- Motivating Examples --- p.3 / Chapter 1.3 --- Contributions --- p.9 / Chapter 1.4 --- Thesis Organization --- p.10 / Chapter 2 --- Problem Definition --- p.11 / Chapter 3 --- Preliminaries --- p.13 / Chapter 3.1 --- Time Warping Distance --- p.13 / Chapter 3.2 --- Constraints and Lower Bounding --- p.16 / Chapter 3.3 --- Uniform Scaling --- p.20 / Chapter 3.3.1 --- Lower bounding uniform scaling --- p.21 / Chapter 4 --- Scaling and Time Warping --- p.23 / Chapter 4.1 --- Tightness of the lower bounds --- p.27 / Chapter 4.2 --- Experimental Evaluation --- p.32 / Chapter 5 --- A Faster and more Flexible Approach --- p.41 / Chapter 5.1 --- The Enveloping Sequences Revisited --- p.41 / Chapter 5.2 --- Speeding up LB Distance Computation --- p.43 / Chapter 5.3 --- Experimental Evaluation --- p.44 / Chapter 5.3.1 --- Query Time Comparison --- p.44 / Chapter 5.3.2 --- Effect on Pruning Power --- p.46 / Chapter 6 --- Indexing for SWM --- p.49 / Chapter 6.1 --- Related Work --- p.49 / Chapter 6.1.1 --- Fast subsequence matching --- p.49 / Chapter 6.1.2 --- Duality-based subsequence matching --- p.50 / Chapter 6.1.3 --- Nearest Neighbor Search --- p.53 / Chapter 6.1.4 --- Dimension Reduction --- p.57 / Chapter 6.2 --- Proposed Indexing for SWM --- p.60 / Chapter 6.2.1 --- Index construction algorithm --- p.60 / Chapter 6.2.2 --- Utilizing the index --- p.61 / Chapter 6.2.3 --- Nearest Neighbor Search --- p.63 / Chapter 6.3 --- Experimental Evaluation --- p.64 / Chapter 6.3.1 --- Range Queries --- p.64 / Chapter 6.3.2 --- One nearest neighbor search --- p.68 / Chapter 6.3.3 --- k-nearest neighbor search --- p.72 / Chapter 7 --- Conclusion --- p.76 / Bibliography --- p.78

Lai, Ka Lun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 75-77). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Multivariate Threshold Time Series Model --- p.14 / Chapter 2.1 --- The Multivariate TAR Models --- p.14 / Chapter 2.2 --- Testing for Nonlinearity --- p.15 / Chapter 2.3 --- Model Selection and Estimation --- p.22 / Chapter 2.4 --- Bivariate TAR Models --- p.26 / Chapter 2.5 --- Applications --- p.27 / Chapter Chapter 3 --- Comparative Study of Interest Rates --- p.34 / Chapter 3.1 --- Background --- p.34 / Chapter 3.2 --- The Importance of Modelling Interest Rates --- p.40 / Chapter 3.3 --- The Scope of Study --- p.41 / Chapter 3.4 --- Major Findings --- p.42 / Chapter Chapter 4 --- Conclusion --- p.71 / Reference --- p.80