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001471924 001__ 1471924
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001471924 019__ $$a1390875532
001471924 020__ $$a9783031318870$$q(electronic bk.)
001471924 020__ $$a3031318870$$q(electronic bk.)
001471924 020__ $$z3031318862
001471924 020__ $$z9783031318863
001471924 0247_ $$a10.1007/978-3-031-31887-0$$2doi
001471924 035__ $$aSP(OCoLC)1390918504
001471924 040__ $$aEBLCP$$beng$$cEBLCP$$dYDX$$dGW5XE$$dEBLCP$$dOCLCQ
001471924 049__ $$aISEA
001471924 050_4 $$aHD30.23
001471924 08204 $$a658.4033$$223/eng/20230728
001471924 1001_ $$aPaczkowski, Walter R.
001471924 24510 $$aPredictive and simulation analytics :$$bdeeper insights for better business decisions /$$cWalter R. Paczkowski.
001471924 260__ $$aCham :$$bSpringer,$$c2023.
001471924 300__ $$a1 online resource (381 p.)
001471924 500__ $$a5 Information Extraction: Non-Time Series Methods
001471924 5050_ $$aIntro -- Preface -- The Target Audience -- The Book's Competitive Comparison -- The Book's Structure -- Acknowledgments -- Contents -- List of Figures -- List of Tables -- Part I The Analytics Quest: The Drive for Rich Information -- 1 Decisions, Information, and Data -- 1.1 Decisions and Uncertainty -- 1.1.1 What Is Uncertainty? -- 1.1.2 The Cost of Uncertainty -- 1.1.3 Reducing Uncertainty -- 1.1.4 The Scale-View of Decision Makers -- 1.1.5 Rich Information Requirements -- 1.2 A Data and Information Framework -- 1.3 Rich Information Predictive Extraction Methods
001471924 5058_ $$a1.3.1 Informal Analytical Components -- 1.3.2 Formal Analytical Components -- 1.4 A Systems Perspective -- 1.5 This Book's Focus -- 2 A Systems Perspective -- 2.1 Introduction to Complex Systems -- 2.2 Types of Systems: Examples -- 2.2.1 Economic Complex Systems -- 2.2.2 Business Complex Systems -- 2.2.3 Other Types of Complex Systems -- 2.3 Predictions, Forecasts, and Business Complex Systems -- 2.4 System Complexity and Scale-View -- 2.5 Simulations and Scale-View -- Part II Predictive Analytics: Background -- 3 Information Extraction: Basic Time Series Methods
001471924 5058_ $$a3.1 Overview of Extraction Methods -- 3.2 Predictions as Time Series -- 3.3 Time Series and Forecasting Notation -- 3.4 The Backshift Operator: An Overview -- 3.5 Naive Forecasting Models -- 3.6 Constant Mean Model -- 3.6.1 Properties of a Variance -- 3.6.2 h-Step Ahead Forecasts -- 3.7 Random Walk Model -- 3.7.1 Basic Random Walk Model -- 3.7.2 Random Walk with Drift -- 3.8 Simple Moving Averages Model -- 3.8.1 Weighted Moving Average Model -- 3.8.2 Exponential Averaging -- 3.9 Linear Trend Models -- 3.9.1 Linear Trend Model Estimation -- 3.9.2 Linear Trend Extension
001471924 5058_ $$a3.9.3 Linear Trend Prediction -- 3.10 Appendix -- 3.10.1 Reproductive Property of Normals -- 3.10.2 Proof of MSE = V() + Bias2 -- 3.10.3 Backshift Operator Result -- 3.10.4 Variance of h-Step Ahead Random Walk Forecast -- 3.10.5 Exponential Moving Average Weights -- 3.10.6 Flat Exponential Averaging Forecast -- 3.10.7 Variance of a Random Variable -- 3.10.8 Background on the Exponential Growth Model -- 4 Information Extraction: Advanced Time Series Methods -- 4.1 The Breadth of Time Series Data -- 4.2 Introduction to Linear Predictive Models -- 4.2.1 Feature Specification
001471924 5058_ $$a4.3 Data Preprocessing -- 4.4 Model Fit vs. Predictability -- 4.5 Case Study: Predicting Total Vehicle Sales -- 4.5.1 Modeling Data: Overview -- 4.5.2 Modeling Data: Some Analysis -- 4.5.3 Linear Model for New Car Sales -- 4.6 Stochastic (Box-Jenkins) Time Series Models -- 4.6.1 Model Identification -- 4.6.2 Brief Introduction to Stationarity -- 4.6.3 Correcting for Non-stationarity -- 4.6.4 Predicting with the AR(1) Model -- 4.7 Advanced Time Series Models -- 4.8 Autoregressive Distributed Lag Models -- 4.8.1 Short-Run and Long-Run Effects -- 4.9 Appendix -- 4.9.1 Chow Test Functions
001471924 506__ $$aAccess limited to authorized users.
001471924 520__ $$aThis book connects predictive analytics and simulation analytics, with the end goal of providing Rich Information to stakeholders in complex systems to direct data-driven decisions. Readers will explore methods for extracting information from data, work with simple and complex systems, and meld multiple forms of analytics for a more nuanced understanding of data science. The methods can be readily applied to business problems such as demand measurement and forecasting, predictive modeling, pricing analytics including elasticity estimation, customer satisfaction assessment, market research, new product development, and more. The book includes Python examples in Jupyter notebooks, available at the book's affiliated Github. This volume is intended for current and aspiring business data analysts, data scientists, and market research professionals, in both the private and public sectors.
001471924 588__ $$aDescription based on print version record.
001471924 650_0 $$aDecision making$$xMathematical models.
001471924 650_0 $$aIndustrial management$$xStatistical methods.
001471924 650_0 $$aIndustrial management$$xData processing.
001471924 655_0 $$aElectronic books.
001471924 77608 $$iPrint version:$$aPaczkowski, Walter R.$$tPredictive and Simulation Analytics$$dCham : Springer International Publishing AG,c2023$$z9783031318863
001471924 77608 $$iPrint version:$$aPACZKOWSKI, WALTER R.$$tPREDICTIVE AND SIMULATION ANALYTICS.$$d[S.l.] : SPRINGER INTERNATIONAL PU, 2023$$z3031318862$$w(OCoLC)1374244065
001471924 852__ $$bebk
001471924 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-31887-0$$zOnline Access$$91397441.1
001471924 909CO $$ooai:library.usi.edu:1471924$$pGLOBAL_SET
001471924 980__ $$aBIB
001471924 980__ $$aEBOOK
001471924 982__ $$aEbook
001471924 983__ $$aOnline
001471924 994__ $$a92$$bISE