... . . . . . . . . . . . . . . . . . . . . Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Keywords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Models for time series 1.1 Time series data . . . . . . . . . . . . . 1.2 Trend, seasonality, cycles and residuals 1.3 Stationary processes . . . . . . . . . . 1.4 Autoregressive processes . . . . . . . . 1.5 Moving average processes . . . . . . . . 1.6 White noise . . . . . . . . . . . . . . . 1.7 The turning point test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii iii iv 1 1 1 1 2 3 4 4 5 5 5 6 6 7 7 8 9 9 9 12 13 13 15 16 17 17 17 18 19 2 Models of stationary processes 2.1 Purely indeterministic processes . . . . . . 2.2 ARMA processes . . . . . . . . . . . . . . 2.3 ARIMA processes . . . . . . . . . . . . . . 2.4 Estimation of the autocovariance function 2.5 Identifying a MA(q) process . . . . . . . . 2.6 Identifying an AR(p) process . . . . . . . . 2.7 Distributions of the ACF and PACF . . . 3 Spectral methods 3.1 The discrete Fourier transform . . . . . . . . . ...
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...Case5 Subhash Sane was the Senior Manager-Retail Operations of a very established hyper mart. It was a Monday afternoon as he stood by the glass door at his office watching people coming in and leaving the store. It was a Monday and there were not too many people other than those who wait for the weekend rush to ebb before they stepped into the store for their week-long groceries He could see one young girl at the footwear section for ladies struggling to decide which pair to buy.| It seemed that she wanted to buy one pair and could not decide which one of the two to buy. Subhash could see her trying one out, walking to the mirror and repeating the same with the other pair. Finally, after the customer service representative of that section had a small conversation with her, the girl happily marched to the till with one pair. Subhash walked back to his chair wondering how similar this incidence was to the situation he was in. He had interviewed three candidates in the last three days for the position of customer service representative. The three candidates did fulfill the basic requirements of the job and did seem to fulfill all the requirements mentioned in the job description. But he could not decide which one to select. There was no way he could try all the candidates out to choose like the girl did with her footwear! And then an idea struck him—'If someone could help that girl decide, I could also do with the advice from someone'. He sat in his chair and called his friend Meera...
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...on Image Processing September 26-29, 2010, Hong Kong K-NEAREST NEIGHBOR SEARCH: FAST GPU-BASED IMPLEMENTATIONS AND APPLICATION TO HIGH-DIMENSIONAL FEATURE MATCHING ´ Vincent Garcia1 , Eric Debreuve2 , Frank Nielsen1,3 , Michel Barlaud2 2 Ecole Polytechnique, Laboratoire d’informatique LIX, 91128 Palaiseau Cedex, France Laboratoire I3S, 2000 route des lucioles, BP 121, 06903 Sophia Antipolis Cedex, France 3 Sony CSL 3-14-13 Higashi Gotanda, Shinagawa-Ku, 141-0022 Tokyo, Japan rithm. Behind its apparent simplicity, this algorithm is highly demanding in terms of computation time. In the last decades, several approaches [4, 5] have been proposed with one common goal: to reduce the computation time. These methods generally seek to reduce the number of distances that have to be computed using, for instance, a pre-arrangement of the data. The direct consequence is a speed-up of the searching process. However, in spite of this improvement, the computation time required by the kNN search still remains the bottleneck of methods based on kNN. General-purpose computing on graphics processing units (GPGPU) is the technique of using a Graphics processing unit (GPU) to perform the computations usually handled by the CPU. The key idea is to use the parallel computing power of the GPU to achieve significant speed-ups. Numerous recent publications use the GPU programming to speed-up their methods [6, 7]. In a previous work [8], we showed that the implementation of the brute-force method...
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...ω(q): fω (x) = ln(10) 10(x+ΥMP, dB )/10 fΥMP 10(x+ΥMP, dB )/10 . 10 Some experimental measurements have also suggested Gaussian to be a good enough yet simple fit for the distribution of ω(q) [17]. We will take advantage of this Gaussian simplification later in our framework. As for the shadowing variable, log-normal is shown to be a good match for the distribution of ΥSH (q). Then, we have the following zero-mean Gaussian pdf 2 1 for the distribution of ν(q): fν (x) = √2πα e−x /2α , where α is the variance of the shadowing variations around path loss. Characterizing the spatial correlation of ω(q) and ν(q) is also considerably important for our model-based channel prediction framework. However, we do not attempt to predict the multipath component, ω(q), due to the fact that it typically decorrelates fast and that the form of its correlation function can change considerably, depending on the angle of arrival and position of the scatterers. Therefore, in our proposed framework we only predict the path loss and shadowing components of the channel. The impact of multipath will then appear in the characterization of the prediction error variance, as we shall see. As for the spatial correlation of shadowing, [18] characterizes an exponentially-decaying spatial correlation function, which is widely used: E ν(q1 )ν(q2 ) = α e− q1 −q2 /β , for q1 , q2 ∈ K where α denotes the shadowing power and the correlation distance, β, controls the spatial correlation of the channel [18]. For some examples...
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...Study Week 2 – Case study: Teenage culture Q: What are the managerial implications of the survey for an international marketer of: a) Potato chips: Clearly kids in the UK have the highest consumption of potato chips – so for a marketer of potato chips the UK market is of course extremely interesting. But I assume the competition within this market segment (kids in the UK) must be equivalent high. Therefor it is crucial that a company within this business, trying to gain market share in the kids segment in the UK, must keep on track with the kids culture – e.g. how do kids in the UK get influenced by various marketing tools and does this differ from kids in other parts of Europe, or the world. The question is how can a potato chips company reach the kids in the UK? Do they have to differentiate their marketing strategy for the UK market? Kids in the UK also have a high rate re. television usage on non-school days. This could be a marketing channel – but might be quite expensive. The kids in Germany seem to have the lowest consumption of potato chips – according to this survey. So a firm trying to gain market share in the potato chips ‘industry’ might see potential new segments in the German market. As the consumption is low compared to kids in the other countries in the survey, there might be a ‘latent need’ for potato chips among German kids. The question is what marketing initiatives will it take to elicit the consumption of potato chips? b) Soft drinks The highest consumption...
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...50 Years of SEM in 50 Minutes?? Karl G J¨reskog o Norwegian Business School & Uppsala University May 11, 2015 Karl G J¨reskog ( ) o 50 Years of SEM in 50 Minutes?? May 11, 2015 1 / 42 Factor Analysis before 1964 Although its roots can be traced back to the work of Francis Galton, it is generally considered that factor analysis began with the celebrated article by Spearman (1904). In the first half of the 20th century factor analysis was mainly developed by psychologists for the purpose of identifying mental abilities by means of psychological testing. Various theories of mental abilities and various procedures for analyzing the correlations among psychological tests emerged. The most prominent factor analysts in the first half of the 20th century seem to be Godfrey Thomson, Cyril Burt, Raymond Cattell, Karl Holzinger, Louis Thurstone and Louis Guttman. A later generation of psychological factor analysts that played important roles are Ledyard Tucker, Ray Cattell, Henry Kaiser, and Chester Harris. Karl G J¨reskog ( ) o 50 Years of SEM in 50 Minutes?? May 11, 2015 2 / 42 Through the 1950’s factor analysis was characterized by a set of ad hoc procedures for analyzing the correlation matrix R of the tests. Four problems of factor analysis emerged: Number of factors Communalities Factor extraction Factor rotation The focus was om computation. Computers were very rare and consisted of large mainframes that filled whole rooms...
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...GENERAL INSURANCE CORPORATION OF INDIA ON-LINE EXAMINATION - RECRUITMENT OF INSURANCE, GENERAL, INFORMATION TECHNOLOGY, FINANCE/ACCOUNTS, ELECTRICAL ENGINEERING, HUMAN RESOURCES DEVELOPMENT, LEGAL, CHEMICAL ENGINEERING, STATISTICS, COMPANY SECRETARY, AERONAUTICAL ENGINEERING, EXECUTIVE P. A., MARINE ENGINEERING, ACTUARY AND MEDICAL INFORMATION HANDOUT This handout contains details pertaining to various aspects of the online main exam you are going to undertake and important instructions about related matters. You are advised to study the handout carefully as it will help you in preparing for the examination. You may have to be at the venue for approximately 4 hours including the time required for logging in, collection of the call letters, going through the instructions etc. The on-line examination will comprise the following objective type multiple choice tests as stated below : Name of the test No. of Questions Maximum Marks Part A Objective Domain Knowledge of the relevant stream 40 40 Part B Test of Reasoning 20 20 Test of English Language 20 20 Test of General Awareness 20 20 Test of Numerical Ability & Computer Literacy 20 20 3 30 123 Time 150 Part C Descriptive Test in English Language Essay, Precise and Comprehension Total 30 minutes 60 minutes for all the tests together 60 minutes PART A AND PART B All tests except test of English Language will be provided in...
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...Brussels is emphasized, the early history of the so-called “Logistic Model” is described. The relationship with older growth models is discussed, and the motivation of Verhulst to introduce different kinds of limited growth models is presented. The (re-)discovery of the chaotic behaviour of the discrete version of this logistic model in the late previous century is reminded. We conclude by referring to some generalizations of the logistic model, which were used to describe growth and diffusion processes in the context of technological innovation, and for which the author studied the chaotic behaviour by means of a series of computer experiments, performed in the eighties of last century by means of the then emerging “micro-computer” technology. 1 P.-F. Verhulst and the Royal Military Academy in Brussels In the year 1844, at the age of 40, when Pierre-Fran¸ois Verhulst on November c 30 presented his contribution to the “M´moires de l’Acad´mie” of the young e e Belgian nation, a paper which was published the next year in “tome XVIII” with the title: “Recherches math´matiques sur la loi d’accroissement de la e population” (mathematical investigations of the law of population growth), he did certainly not know that his work would be the starting point for further research by Raymond Pearl and Lowell J. Reed [10, 11], by the famous A.-J. Lotka [8] and independently by Volterra [16] and later by V.A. Kostitzin [5], in the fields of mathematical biology, biometry, and demography. It was in...
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...Work in Progress: Lecture Notes on the Status of IEEE 754 October 1, 1997 3:36 am Lecture Notes on the Status of IEEE Standard 754 for Binary Floating-Point Arithmetic Prof. W. Kahan Elect. Eng. & Computer Science University of California Berkeley CA 94720-1776 Introduction: Twenty years ago anarchy threatened floating-point arithmetic. Over a dozen commercially significant arithmetics boasted diverse wordsizes, precisions, rounding procedures and over/underflow behaviors, and more were in the works. “Portable” software intended to reconcile that numerical diversity had become unbearably costly to develop. Thirteen years ago, when IEEE 754 became official, major microprocessor manufacturers had already adopted it despite the challenge it posed to implementors. With unprecedented altruism, hardware designers had risen to its challenge in the belief that they would ease and encourage a vast burgeoning of numerical software. They did succeed to a considerable extent. Anyway, rounding anomalies that preoccupied all of us in the 1970s afflict only CRAY X-MPs — J90s now. Now atrophy threatens features of IEEE 754 caught in a vicious circle: Those features lack support in programming languages and compilers, so those features are mishandled and/or practically unusable, so those features are little known and less in demand, and so those features lack support in programming languages and compilers. To help break that circle, those features are discussed in these notes...
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...Software. Topic models allow the probabilistic modeling of term frequency occurrences in documents. The fitted model can be used to estimate the similarity between documents as well as between a set of specified keywords using an additional layer of latent variables which are referred to as topics. The R package topicmodels provides basic infrastructure for fitting topic models based on data structures from the text mining package tm. The package includes interfaces to two algorithms for fitting topic models: the variational expectation-maximization algorithm provided by David M. Blei and co-authors and an algorithm using Gibbs sampling by Xuan-Hieu Phan and co-authors. Keywords: Gibbs sampling, R, text analysis, topic model, variational EM. 1. Introduction In machine learning and natural language processing topic models are generative models which provide a probabilistic framework for the term frequency occurrences in documents in a given corpus. Using only the term frequencies assumes that the information in which order the words occur in a document is negligible. This assumption is also referred to as the exchangeability assumption for the words in a document and this assumption leads to bag-of-words models. Topic models extend and build on classical methods in natural language processing such as the unigram model and the mixture of unigram models (Nigam, McCallum, Thrun, and Mitchell 2000) as well as Latent Semantic Analysis (LSA; Deerwester, Dumais, Furnas, Landauer, and Harshman...
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...Muffler Magic Case Study 12/19/2009 Human Resource Management Shahmeer Qasim Company Introduction This particular case is about an automobile service centers which was located in Nevada. The owner was Ronald Brown who started with a single store 20 years ago for repairing mufflers. Which eventually grew into a chain of repair shops and expended their services from muffler replacement to oil changes, brake jobs and engine repairs. Organizational Structure • Organizationally , Muffler Magic employees about 300 people total, and Ron runs his company with eighth managers including MR. Brown as president, a controller, a purchasing director, a marketing director and the human resource manager. • He also has three regional managers to whom the eighth or nine service center managers in each area of Nevada report. Case Analysis Problem & Challenges • Being a service oriented company; the shop owner was basically dependent upon the quality of the service people he or she hires or retain. • Quality was a persistent problem as well. (rework) • Problems like replacement was diminishing the profitability of the company and same reputed many times over have the potential for ruining Muffler Magic’s word of mouth reputation. • The company had only the most basic HR system in place and used only an application form that the HR manger modified from one that was downloaded from the web and other forms purchased from a human resource management supply...
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...AOMEI® Partition Assistant 5.1 AOMEI® Partition Assistant 5.1 User Manual Copyright © 2009-2012 AomeiTech. All rights reserved. 1 AOMEI® Partition Assistant 5.1 Welcome........................................................................................................................................3 About AOMEI Partition Assistant.....................................................................................3 Working Computer Configuration.....................................................................................4 Product Main Window ......................................................................................................5 Partition Operation Properties...........................................................................................5 Partition Operations ......................................................................................................................6 Resize and Move Partition ................................................................................................6 Merge Partitions ................................................................................................................7 Split Partition ....................................................................................................................7 Allocate Free Space...........................................................................................................8 Copy Partition .............................
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...SURVEY OF SYNOPSIS CONSTRUCTION IN DATA STREAMS Abstract The large volume of data streams poses unique space and time constraints on the computation process. Many query processing, database operations, and mining algorithms require efficient execution which can be difficult to achieve with a fast data stream. In many cases, it may be acceptable to generate approximate solutions for such problems. In recent years a number of synopsis structures have been developed, which can be used in conjunction with a variety of mining and query processing techniques in data stream processing. Some key synopsis methods include those of sampling, wavelets, sketches and histograms. In this chapter, we will provide a survey of the key synopsis techniques, and the mining techniques supported by such methods. We will discuss the challenges and tradeoffs associated with using different kinds of techniques, and the important research directions for synopsis construction. 1. Introduction Data streams pose a unique challenge to many database and data mining applications because of the computational and storage costs associated with the large volume of the data stream. In many cases, synopsis data structures 170 DATA STREAMS: MODELS AND ALGORITHMS and statistics can be constructed from streams which are useful for a variety of applications. Some examples of such applications are as follows: Approximate Query Estimation: The problem of query estimation is possibly the...
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...Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with the Invariant Sections being “GNU General Public License” and “Free Software Needs Free Documentation”, the Front-Cover text being “A GNU Manual”, and with the Back-Cover Text being (a) (see below). A copy of the license is included in the section entitled “GNU Free Documentation License”. (a) The Back-Cover Text is: “You have the freedom to copy and modify this GNU Manual.” Printed copies of this manual can be purchased from Network Theory Ltd at http://www.network-theory.co.uk/gsl/manual/. The money raised from sales of the manual helps support the development of GSL. i Table of Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 Routines available in GSL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GSL is Free Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....
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...Algorithms in 24 Hours Copyright © 1999 by Sams Publishing All rights reserved. No part of this book shall be reproduced, stored in a retrieval system, or transmitted by any means, electronic, mechanical, photocopying, recording, or otherwise, without written permission from the publisher. No patent liability is assumed with respect to the use of the information contained herein. Although every precaution has been taken in the preparation of this book, the publisher and author assume no responsibility for errors or omissions. Neither is any liability assumed for damages resulting from the use of the information contained herein. International Standard Book Number: 0-672-31633-1 Library of Congress Catalog Card Number: 98-83221 Printed in the United States of America First Printing: May 1999 01 00 99 4 3 2 1 EXECUTIVE EDITOR Brian Gill DEVELOPMENT EDITOR Jeff Durham MANAGING EDITOR Jodi Jensen PROJECT EDITOR Tonya Simpson COPY EDITOR Mike Henry INDEXER Larry Sweazy PROOFREADERS Mona Brown Jill Mazurczyk TECHNICAL EDITOR Richard Wright Trademarks All terms mentioned in this book that are known to be trademarks or service marks have been appropriately capitalized. Sams Publishing cannot attest to the accuracy of this information. Use of a term in this book should not be regarded as affecting the validity of any trademark or service mark. SOFTWARE DEVELOPMENT SPECIALIST Dan Scherf INTERIOR DESIGN Gary Adair Warning and Disclaimer Every...
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