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Videos

https://www.youtube.com/playlist?list=PLoK4oIB1jeK0LHLbZW3DTT05e4srDYxFq
A series of 39 videos by Galit Shmueli is available to support the book.
The videos are available on YouTube.
Here is the list of the videos and the corresponding book chapter.

          Intro
  1. Business Analytics Using Forecasting (Intro) (3:36)

    Chapter 1

  2. Intro: Forecasting language & notation (7:54)
  3. Intro: Forecasting future values vs. past values (8:20)
  4. Interviews: Interview with YouBike spokesperson about forecasting (4:43)
  5. Interviews: Interview with NTHU VP for General Affairs about forecasting (5:34)
  6. Forecasting electricity load: Interview with Prof. Tao Hong (5:46)
  7. Interviews: Interview with NTHU library Collection Division Head about forecasting (4:47)
  8. Interview with manager at Smile Cafe (at NTHU) (1:40)

    Chapter 2

  9. Exploring time series using visualization (with Tableau) (10:39)

    Chapter 3

  10. Performance 1: Data partitioning for time series (7:11)
  11. Performance 2: Using software for partitioning time series (3:38)
  12. Performance 3: Naive forecasts (4:36)
  13. Performance 4: Predictive metrics & charts (20:32)

    Chapter 5

  14. Smoothing 1: Moving Average for visualization (5:01)
  15. Smoothing 2: Moving Average for forecasting (11:09)
  16. Smoothing 3: Differencing (4:34)
  17. Smoothing 4: Simple exponential smoothing (SES) (12:21)
  18. Smoothing 5: Holt's exponential smoothing (11:59)
  19. Smoothing 6: Winter's exponential smoothing (9:18)
  20. Smoothing 7: Automation and prediction intervals (3:25)

    Chapter 6

  21. Regression 1: Regression for forecasting (4:48)
  22. Regression 2: Linear trend model (7:04)
  23. Regression 3: Other trend models (8:16)
  24. Regression 4: Model for capturing seasonality (8:01)

    Chapter 7

  25. Autocorrelation (6:57)
  26. Autoregressive model for forecast errors (11:14)
  27. ARIMA Models (12:02)
  28. Regression: Including external information (Part A) (8:38)
  29. Regression: Including external information (Part B) (13:36)
  30. Forecasting and big data: Interview with Prof. Rob Hyndman (7:27)
  31. Concluding the journey (5:55)

    Chapter 8

  32. Binary Forecasts: Part A (9:35)
  33. Binary Forecasts: Part B (7:01)
  34. Forecasting with Logistic Regression: Part A (3:01)
  35. Forecasting with Logistic Regression: Part B (9:16)

    Chapter 9

  36. Forecasting with Neural Networks: Part A (11:48)
  37. Forecasting with Neural Networks: Part B (10:33)
  38. Forecasting with Neural Networks: Part C (7:15)

    Chapter 10

  39. Communication and Maintenance (13:30)