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Profit from your forecasting software : a best practice guide for sales forecasters /
With a focus on choice, interpretation, and judgement, this practical book goes beyond the technical manuals to help you truly grasp the more intangible skills of forecasting that lead to better accuracy. --
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Inc.,
[2018]
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| Colección: | Wiley & SAS business series
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Cover; Title Page; Copyright; Contents; Acknowledgments; Prologue; Chapter 1: Profit from Accurate Forecasting; 1.1 The Importance of Demand Forecasting; 1.2 When Is a Forecast Not a Forecast?; 1.3 Ways of Presenting Forecasts; 1.3.1 Forecasts as Probability Distributions; 1.3.2 Point Forecasts; 1.3.3 Prediction Intervals; 1.4 The Advantages of Using Dedicated Demand Forecasting Software; 1.5 Getting Your Data Ready for Forecasting; 1.6 Trading-Day Adjustments; 1.7 Overview of the Rest of the Book; 1.8 Summary of Key Terms; 1.9 References.
- Chapter 2: How Your Software Finds Patterns in Past Demand Data2.1 Introduction; 2.2 Key Features of Sales Histories; 2.2.1 An Underlying Trend; 2.2.2 A Seasonal Pattern; 2.2.3 Noise; 2.3 Autocorrelation; 2.4 Intermittent Demand; 2.5 Outliers and Special Events; 2.6 Correlation; 2.7 Missing Values; 2.8 Wrap-Up; 2.9 Summary of Key Terms; Chapter 3: Understanding Your Software's Bias and Accuracy Measures; 3.1 Introduction; 3.2 Fitting and Forecasting; 3.2.1 Fixed-Origin Evaluations; 3.2.2 Rolling-Origin Evaluations; 3.3 Forecast Errors and Bias Measures; 3.3.1 The Mean Error (ME).
- 3.3.2 The Mean Percentage Error (MPE)3.4 Direct Accuracy Measures; 3.4.1 The Mean Absolute Error (MAE); 3.4.2 The Mean Squared Error (MSE); 3.5 Percentage Accuracy Measures; 3.5.1 The Mean Absolute Percentage Error (MAPE); 3.5.2 The Median Absolute Percentage Error (MDAPE); 3.5.3 The Symmetric Mean Absolute Percentage Error (SMAPE); 3.5.4 The MAD/MEAN Ratio; 3.5.5 Percentage Error Measures When There Is a Trend or Seasonal Pattern; 3.6 Relative Accuracy Measures; 3.6.1 Geometric Mean Relative Absolute Error (GMRAE); 3.6.2 The Mean Absolute Scaled Error (MASE).
- 3.6.3 Bayesian Information Criterion (BIC)3.7 Comparing the Different Accuracy Measures; 3.8 Exception Reporting; 3.9 Forecast Value-Added Analysis (FVA); 3.10 Wrap-Up; 3.11 Summary of Key Terms; 3.12 References; Chapter 4: Curve Fitting and Exponential Smoothing; 4.1 Introduction; 4.2 Curve Fitting; 4.2.1 Common Types of Curve; 4.2.2 Assessing How Well the Curve Fits the Sales History; 4.2.3 Strengths and Limitations of Forecasts Based on Curve Fitting; 4.3 Exponential Smoothing Methods; 4.3.1 Simple (or Single) Exponential Smoothing.
- 4.3.2 Exponential Smoothing When There Is a Trend: Holt's Method4.3.3 The Damped Holt's Method; 4.3.4 Holt's Method with an Exponential Trend; 4.3.5 Exponential Smoothing Where There Is a Trend and Seasonal Pattern: The Holt-Winters Method; 4.3.6 Overview of Exponential Smoothing Methods; 4.4 Forecasting Intermittent Demand; 4.5 Wrap-Up; 4.6 Summary of Key Terms; Chapter 5: Box-Jenkins ARIMA Models; 5.1 Introduction; 5.2 Stationarity; 5.3 Models of Stationary Time Series: Autoregressive Models; 5.4 Models of Stationary Time Series: Moving Average Models.