Table of Content - Time Series Based Predictive Analytics Modelling in Excel
Introduction                      
1. Predictive analytics in a broader context              
   1.1 Data mining and Data analytics
   1.2 Business Intelligence and Decision Support Systems
   1.3 Predictive analytics subset
   1.4 Relationship between forecasting, planning and decision-making
2. Predictive analytics modelling rationale and basic ideas              
   2.1 Time series based forecasting models
   2.2 Forecasting horizon
   2.3 Data inspection and preparation
3. The role of error measurement                     
   3.1 Error measurement
   3.2 Types of errors
   3.3 Other error metrics
4. Understanding basic techniques              
   4.1 Averaging
   4.2 Differencing
   4.3 Transformations
   4.4 Smoothing
5. Associations between and within the time series                
   5.1 Correlation
   5.2 Autocorrelation
   5.3 Interpreting autocorrelations
   5.4 Corrections to the autocorrelations standard error
   5.5 Partial Autocorrelation
   5.6 Using Excel functions to calculate partial autocorrelations
   5.7 Standard errors for the partial autocorrelations
   5.8 Autocorrelations evaluation methods
6. Long-term models - Fitting underlying trend and regression 
   6.1 Visualisation
   6.2 Time series classification
   6.3 Curve classification
   6.4 Recognising curves
   6.5 Fitting curves and extrapolating
   6.6 Simple Regression
      6.6.1 Variations around the regression line
      6.6.2 Estimating parameters
      6.6.3 Using Excel Regression Add-in
      6.6.4 Basic confidence interval
      6.6.5 Changing confidence interval
      6.6.6 The alternative confidence interval calculation
   6.7 Inspecting Errors in Regression Analysis
      6.7.1 Durbin-Watson test
      6.7.2 Homoscedasticity
7. Short-term models
   7.1 Simple averaging techniques
   7.2 Exponential Smoothing
      7.2.1 The Standard Errors
8. Medium-term models
   8.1 Double moving averages (DMA)
   8.2 Double exponential smoothing (DES)
   8.3 Triple exponential smoothing (TES)
   8.4 Holt’s Double exponential smoothing method
   8.5 Damped trend exponential smoothing methods
9. Seasonal models                   
   9.1. Classical decomposition method
   9.2 Seasonal adjustments and exponential smoothing
   9.3 Simple seasonal exponential smoothing
   9.4 Holt-Winters seasonal method
10. State space models                   
   10.1 Defining a state space model
   10.2 Excel implementation of the ETS(A,A,A) models
   10.3 Manual state space modelling in Excel
   10.4 Model selection and forecasting with ETS models
11. Stochastic models                         
   11.1 Types of models
   11.2 Model identification
      11.2.1 Example of model identification
      11.2.2 Estimating autocorrelations
      11.2.3 The question of differencing
      11.2.4 Testing for zero mean
   11.3 Box Jenkins approach to ARIMA Modelling
      11.3.1 Basic AR models
      11.3.2 Higher AR models
      11.3.3 MA models
      11.3.4 Mixed ARMA models
      11.3.5 Permissible regions
      11.3.6 Parameter redundancy
   11.4 Model fitting and forecasting
      11.4.1 AR(1) forecasts
      11.4.2 MA(1) forecasts
      11.4.3 ARIMA(1,1,1) forecasts
      11.4.4 Forecasting and fitting example
   11.5 Model building
   11.6 Diagnostic checking
   11.7 Seasonal ARIMA models
   11.8 Applying the Box-Jenkins method using NumXL Excel add-in
12. Forecasting process              
   12.1 Selecting the appropriate method
   12.2 Qualifying forecasts
   12.3 Residual analysis
   12.4 Monitoring forecasts (CUSUM)
   12.5 Forecast selection
13. Advanced forecasting topics  
   13.1 Frequency domain analysis    
   13.2 State space models and the Kalman filtering
   13.3 Artificial Neural Networks
   13.4 Fuzzy Logic
   13.5 Chaos theories
   13.6 Econometric time series analysis
   13.7 Forecasting in the supply chain
Appendix 1 Understanding Excel basics
   Referencing cells
   Functions
   Charts and graphs
   Combined charts
   Excel Add-ins
Appendix 2 Understanding elementary statistical concepts   Histograms
   Descriptive statistics
   Two variables
   Samples and distributions
   Confidence interval
Bibliography                                                                                                                        
Index                                                                                                                                    
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You can view Chapter 5 and Chapter 13.1 as an example to illustrate the style and the complexity of the material.