ForecastIT 1. Introduction to Forecasting

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This lesson begins with background information about forecasting and continues discussing the forecasting process. We concentrate on four main steps: set an objective, build model, evaluate model, and use model.

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ForecastIT 1. Introduction to Forecasting

  1. 1. Introduction to Forecasting<br />Lesson #1<br />Introduction to Forecasting<br />Using quantitative methods<br />Copyright 2010 DeepThought, Inc.<br />1<br />
  2. 2. Introduction to Forecasting<br />Quantitative vs. Qualitative Forecasting Methods<br /><ul><li>Quantitative
  3. 3. Universal meaning
  4. 4. Widely used in business
  5. 5. Easy to evaluate</li></ul>Vs.<br /><ul><li>Qualitative
  6. 6. Build on personal experience
  7. 7. No data needed
  8. 8. Hard to measure</li></ul>Copyright 2010 DeepThought, Inc.<br />2<br />
  9. 9. Introduction to Forecasting<br />Quantitative Methods<br /><ul><li>Take upon multiple forms
  10. 10. Linear regression: Y = a + b × x
  11. 11. Exponential smoothing: F(t+1) = a × A(t) + (1-a) × F(t)
  12. 12. Moving average, multivariable, etc.
  13. 13. Have Multiple Purposes
  14. 14. Forecasting
  15. 15. Trend, seasonality, and cyclical estimation
  16. 16. Evaluating variable relationships
  17. 17. And more…</li></ul>Copyright 2010 DeepThought, Inc.<br />3<br />
  18. 18. Introduction to Forecasting<br />Role of statistics in quantitative forecasting<br /><ul><li>Each variable is assumed to be independent of each other, and random
  19. 19. The central limit theorem states that if a variable has 30 or more observations, we can use the normal distribution approximation to evaluate its mean and variance
  20. 20. Enables us to test the statistical significance of the model and its coefficients</li></ul>Copyright 2010 DeepThought, Inc.<br />4<br />
  21. 21. Introduction to Forecasting<br />Method Selection<br /><ul><li>Each forecasting method has its own characteristics
  22. 22. Each method can answer different questions
  23. 23. Some methods should only be used in specific situations
  24. 24. Method selection steps:
  25. 25. Select dependent variable
  26. 26. Select units
  27. 27. Select time frame
  28. 28. Select what method to use
  29. 29. Adjusted units if needed to fit method
  30. 30. Adjust how many periods to forecast depending on method</li></ul>Copyright 2010 DeepThought, Inc.<br />5<br />
  31. 31. Introduction to Forecasting<br />Model Building<br /><ul><li>Most methods try to fit themselves to the data
  32. 32. Minimizing the sum of squared errors
  33. 33. A sum of square difference of actual data points compared to model produced data points
  34. 34. Creates the best fit model using that specific forecasting method
  35. 35. Model statistics are produced for model evaluate</li></ul>Copyright 2010 DeepThought, Inc.<br />6<br />
  36. 36. Introduction to Forecasting<br />Model Evaluation<br /><ul><li>Looking at the statistics of a model help us determine if the model makes sense or not and how accurate it is
  37. 37. The same statistics are used for all type of methods
  38. 38. Enables us to compare multiple models to find the best models</li></ul>Copyright 2010 DeepThought, Inc.<br />7<br />
  39. 39. Introduction to Forecasting<br />Forecasting Steps<br />Set an objective<br />Build model<br />Evaluate model<br />Use model<br />Copyright 2010 DeepThought, Inc.<br />8<br />
  40. 40. Introduction to Forecasting<br />1. Objective Setting<br /><ul><li>Keep it Simple
  41. 41. Set a clear objective such as:
  42. 42. Find best forecast for gas prices
  43. 43. Find the overall trend of gas prices from multiple forecast models
  44. 44. Find seasonal indices for gas prices
  45. 45. Find relationships (or lack of) between variables gas prices (dependent), and GDP(independent), interest rate(independent)…</li></ul>Copyright 2010 DeepThought, Inc.<br />9<br />
  46. 46. Introduction to Forecasting<br />2. Building Models<br /><ul><li>Understand the type of data you have
  47. 47. Does it have a trend, seasonality, or cyclicality
  48. 48. Select the appropriate methods to use given the objective and data type
  49. 49. Use (economic, financial, est.) theory to back your model
  50. 50. Start with simple models
  51. 51. Build on good simple models</li></ul>Copyright 2010 DeepThought, Inc.<br />10<br />
  52. 52. Introduction to Forecasting<br />3. Evaluating Models<br /><ul><li>Statistical significance
  53. 53. F-Test
  54. 54. F-Test P-Value
  55. 55. Rule of thumb: F-Test P-Value < 0.05
  56. 56. Accuracy (the lower the better except R2)
  57. 57. SSE: Sum of Squared Errors
  58. 58. RMSE: Root Mean Square Error
  59. 59. MAPE: Mean Average Percentage Error
  60. 60. R2/ Adjusted R2: % Error Captured by Model</li></ul>Copyright 2010 DeepThought, Inc.<br />11<br />
  61. 61. Introduction to Forecasting<br />4. Using Models<br /><ul><li>Use best models for:
  62. 62. Forecasting
  63. 63. Understanding trend, seasonality, and cyclicality
  64. 64. Understanding relationships between different variables</li></ul>Copyright 2010 DeepThought, Inc.<br />12<br />

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