Predictive and simulation analytics : deeper insights for better business decisions

Predictive and simulation analytics : deeper insights for better business decisions / Walter R. Paczkowski.

This 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 mult...

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Bibliographic Details
Main Author: Paczkowski, Walter R.
Format: eBook
Language:English
Published: Cham : Springer, 2023.
Subjects:
Decision making > Mathematical models.
Industrial management > Statistical methods.
Industrial management > Data processing.
Decision making > Mathematical models
Industrial management > Data processing
Industrial management > Statistical methods
Presa de decisions.
Models matemàtics.
Direcció d'empreses.
Estadística matemàtica.
Matemàtica discreta.
Llibres electrònics.
Online Access:Click for online access
Table of Contents:
  • Intro
  • 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
  • 1.3.1 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
  • 3.1 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
  • 3.9.3 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
  • 4.3 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

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