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  1. 1. Forecasting
  2. 2. Why Forecast ? To Plan For Demand Its not a goal
  3. 3. Vocabulary of Time Series Trend – gradual upward or downward movement of the data over time Seasonality – demand fluctuation pattern above and below the trend line repeating at certain points of time ( not limited to years) example – business at the bank with seasonal effects around mid and end of month paydays Cycles – patterns that occur in the data every several years (reflect business cycles) Random Variations ( Special Causes )– chance variation in the data – no pattern – like error in statistical model
  4. 4. Graph of a Time Series
  5. 5. Analyse data Univariate A No Yes Plot Data into I-MR Chart Huge Variations No Correct For Special Causes Decompose Stationary ? Trend & Seasonality Single Exponential Smoothening Double Exponential Smoothening Holt -Winters Yes No No Stable after Checking for n/2 vaues/ errors? Forecast Value Regression MAPE ,MAD, MSE Should be very good pointers Yes Yes Yes Yes No
  6. 6. Illustration Of Errors
  7. 7. Measures of Forecast Error <ul><li>Bias - The arithmetic sum of the errors </li></ul><ul><li>MAD - Mean Absolute Deviation </li></ul><ul><li>MAPE – Mean Absolute Percentage Error </li></ul><ul><li>Mean Square Error (MSE) - Similar to simple sample variance </li></ul><ul><li>Standard Error - Standard deviation of the sampling distribution (the square </li></ul><ul><li>root of the MSE) </li></ul><ul><li>Bias, MAD, and MAPE - typically </li></ul><ul><li>used for time series </li></ul>Minitab Gives all 
  8. 9. THANK YOU !!!! DIrect LIase CONfirm