Profit from Your Forecasting Software : A Best Practice Guide for Sales Forecasters

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 Data -- 2.1 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).