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008			101215s2011 njua b 001 0 eng
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040			_aDLC _beng _cDLC _dYDX _dYDXCP _dCDX _dUKMGB _dBWX _dBAUN _erda
049			_aBAUN_MERKEZ
050	0	4	_aQA280 _b.B575 2011
082	0	0	_222
100	1		_aBisgaard, Soren, _d1938-
245	1	0	_aTime series analysis and forecasting by example / _cSøren Bisgaard, Murat Kulahci
264		1	_aHoboken, N.J. : _bJohn Wiley and Sons, _c[2011]
264		4	_c©2011
300			_axiii, 366 pages : _billustrations, _c25 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	1		_aWiley series in probability and statistics
504			_aIncludes bibliographical references and index
505	0	0	_tTable Of Contents: _tPreface _t1 Time Series Data: Examples And Basic Concepts _t1.1 Introduction _t1.2 Examples of Time Series Data _t1.3 Understanding Autocorrelation _t1.4 The Wold Decomposition _t1.5 The Impulse Response Function _t1.6 Superposition Principle _t1.7 Parsimonious Models _tExercises _t2 Visualizing Time Series Data Structures: Graphical Tools _t2.1 Introduction _t2.2 Graphical Analysis of Time Series _t2.3 Graph Terminology _t2.4 Graphical Perception _t2.5 Principles of Graph Construction _t2.6 Aspect Ratio _t2.7 Time Series Plots _t2.8 Bad Graphics _tExercises _t3 Stationary Models _t3.1 Basics of Stationary Time Series Models _t3.2 Autoregressive Moving Average (ARMA) Models _t3.3 Stationarity and Invertibility of ARMA Models _t3.4 Checking for Stationarity using Variogram _t3.5 Transformation of Data _tExercises _t4 Nonstationary Models _t4.1 Introduction _t4.2 Detecting Nonstationarity _t4.3 Autoregressive Integrated Moving Average (ARIMA) Models _t4.4 Forecasting using ARIMA Models _t4.5 Example 2: Concentration Measurements from a Chemical Process _t4.6 The EWMA Forecast _tExercises _t5 Seasonal Models _t5.1 Seasonal Data _t5.2 Seasonal ARIMA Models _t5.3 Forecasting using Seasonal ARIMA Models _t5.4 Example 2: Company X's Sales Data _tExercises _t6 Time Series Model Selection _t6.1 Introduction _t6.2 Finding the "BEST" Model _t6.3 Example: Internet Users Data _t6.4 Model Selection Criteria _t6.5 Impulse Response Function to Study the Differences in Models _t6.6 Comparing Impulse Response Functions for Competing Models _t6.7 ARIMA Models as Rational Approximations _t6.8 AR Versus Arma Controversy _t6.9 Final Thoughts on Model Selection _tAppendix 6.1 How to Compute Impulse Response Functions with a Spreadsheet _tExercises _t7 Additional Issues In Arima Models _t7.1 Introduction _t7.2 Linear Difference Equations _t7.3 Eventual Forecast Function _t7.4 Deterministic Trend Models _t7.5 Yet Another Argument for Differencing _t7.6 Constant Term in ARIMA Models _t7.7 Cancellation of Terms in ARIMA Models _t7.8 Stochastic Trend: Unit Root Nonstationary Processes _t7.9 Overdifferencing and Underdifferencing _t7.10 Missing Values in Time Series Data _tExercises _t8 Transfer Function Models _t8.1 Introduction _t8.2 Studying Input-Output Relationships _t8.3 Example 1: The Box-Jenkins' Gas Furnace _t8.4 Spurious Cross Correlations _t8.5 Prewhitening _t8.6 Identification of the Transfer Function _t8.7 Modeling the Noise _t8.8 The General Methodology for Transfer Function Models _t8.9 Forecasting Using Transfer Function-Noise Models _t8.10 Intervention Analysis _tExercises _t9 Additional Topics _t9.1 Spurious Relationships _t9.2 Autocorrelation in Regression _t9.3 Process Regime Changes _t9.4 Analysis of Multiple Time Series _t9.5 Structural Analysis of Multiple Time Series _tExercises _tAppendix A DATASETS USED IN THE EXAMPLES _tTable A.1 Temperature Readings from a Ceramic Furnace _tTable A.2 Chemical Process Temperature Readings _tTable A.3 Chemical Process Concentration Readings _tTable A.4 International Airline Passengers _tTable A.5 Company X's Sales Data _tTable A.6 Internet Users Data _tTable A.7 Historical Sea Level (mm) Data in Copenhagen, Denmark _tTable A.8 Gas Furnace Data _tTable A.9 Sales with Leading Indicator _tTable A.10 Crest/Colgate Market Share _tTable A.11 Simulated Process Data _tTable A.12 Coen and others (1969) Data _tTable A.13 Temperature Data from a Ceramic Furnace _tTable A.14 Temperature Readings from an Industrial Process _tTable A.15 US Hog Series _tAppendix B DATASETS USED IN THE EXERCISES _tTable B.1 Beverage Amount (ml) _tTable B.2 Pressure of the Steam Fed to a Distillation Column (bar) _tTable B.3 Number of Paper Checks Processed in a Local Bank _tTable B.4 Monthly Sea Levels in Los Angeles, California (mm) _tTable B.5 Temperature Readings from a Chemical Process (°C) _tTable B.6 Daily Average Exchange Rates between US Dollar and Euro _tTable B.7 Monthly US Unemployment Rates _tTable B.8 Monthly Residential Electricity Sales (MWh) and Average Residential Electricity Retail Price (c/kWh) in the United States _tTable B.9 Monthly Outstanding Consumer Credits Provided by Commercial Banks in the United States (million USD) _tTable B.10 100 Observations Simulated from an ARMA (1, 1) Process _tTable B.11 Quarterly Rental Vacancy Rates in the United States _tTable B.12 Wolfer Sunspot Numbers _tTable B.13 Viscosity Readings from a Chemical Process _tTable B.14 UK Midyear Population _tTable B.15 Unemployment and GDP data for the United Kingdom _tTable B.16 Monthly Crude Oil Production of OPEC Nations _tTable B.17 Quarterly Dollar Sales of Marshall Field and Company ([dollars]1000) _tBibliography _tIndex
520			_aTechnology management scholar (U. of Massachusetts-Amherst) Bisgaard (1938-2010) and Kulahci (statistics, Technical U. of Denmark) found that many students and practitioners in statistics get frustrated trying to learn time series analysis, and either give up on it entirely or just plug data into a software package and accept what comes out. They set out to provide an introduction that is easy to understand and use, and that draws heavily from examples to demonstrate the principles and techniques. The profession of statistics needs at least a few people who know what is actually going on, they say, and who know the shortfalls of the statistical techniques being used. Annotation ©2011 Book News, Incorporated, Portland, OR (booknews.com)
520			_aAn intuition-based approach enables you to master time series analysis with ease
520			_aTime Series Analysis and Forecasting by Example provides the fundamental techniques in time series analysis using various examples. By introducing necessary theory through examples that showcase the discussed topics, the authors successfully help readers develop an intuitive understanding of seemingly complicated time series models and their implications.
520			_aThe book presents methodologies for time series analysis in a simplified, example-based approach. Using graphics, the authors discuss each presented example in detail and explain the relevant theory while also focusing on the interpretation of results in data analysis. Following a discussion of why autocorrelation is often observed when data is collected in time, subsequent chapters explore related topics, including:
520			_aGraphical tools in time series analysis Procedures for developing stationary, non-stationary, and seasonal models How to choose the best time series model Constant term and cancellation of terms in ARIMA models Forecasting using transfer function-noise models
520			_aThe final chapter is dedicated to key topics such as spurious relationships, autocorrelation in regression, and multiple time series. Throughout the book, real-world examples illustrate step-by-step procedures and instructions using statistical software packages such as SAS®, JMP, Minitab, SCA, and R. A related Web site features PowerPoint slides to accompany each chapter as well as the book's data sets.
520			_aWith its extensive use of graphics and examples to explain key concepts, Time Series Analysis and Forecasting by Example is an excellent book for courses on time series analysis at the upper-undergraduate and graduate levels. it also serves as a valuable resource for practitioners and researchers who carry out data and time series analysis in the fields of engineering, business, and economics.
650		0	_aTime-series analysis
650		0	_aForecasting
700	1		_aKulahci, Murat
900			_a31554
900			_bsatın
942			_2lcc _cKT
999			_c28438 _d28438