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4/16/ Ardavan Asef-Vaziri Variable of interest Time Series Analysis

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Components of an Observation 4/16/2015Ardavan Asef-Vaziri 2 Observed variable (O) = Systematic component (S) + Random component (R) Level (current deseasonalized ) Trend (growth or decline) Seasonality (predictable seasonal fluctuation) Systematic component: Expected value of the variable Random component: The part of the forecast that deviates from the systematic component Forecast error: difference between forecast and actual demand

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A t : Actual valued in period t F (t+1) : Forecast for period t+1 F (t+1) = A t Naive Forecast The naive forecast can also serve as an accuracy standard for other techniques. 4/16/ Ardavan Asef-Vaziri

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Moving Average Three period moving average in period 7 is the average of: MA 7 3 = (A 7 + A 6 + A 5 )/3 4/16/ Ardavan Asef-Vaziri MA t 10 = (A t + A t-1 + A t-2 +A t-3 + ….+ A t-9 )/10 Ten period moving average in period t is the average of: n period moving average in period t is the average of: MA t n = (A t + A t-1 + A t-2 +A t-3 + ….+ A t-n+1 )/n Forecast for period t+1 is equal to moving average for period t F t+1 =MA t n

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MA 4 21 = (A21+A20+A19+A18)/4 MA 4 21 = ( )/4=725 4-Period Moving Average at period 20, and 21 It was used as forecast for period 21. The actual values in period 21 is 800 The Actual cost of a specific task type for periods was 600, 700, 680, 720, respectively 4/16/ Ardavan Asef-Vaziri MA 4 20 = (A20+A19+A18+A17)/4 MA 4 20 = ( )/4 = 675 MA 4 21 = MA (A21- A17)/4 MA 4 21 = 675 +( ) /4=725

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Micro $oft Stock 4/16/ Ardavan Asef-Vaziri AS n increases, we obtain a smoother curve

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Exponential Smoothing 4/16/ Ardavan Asef-Vaziri

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Exponential Smoothing α=.2 t At Ft A1 F Since I have no information for F2, I just enter A1 which is F3 =(1-α)F2 + α A2 F3 =.8(100) +.2(150) F3 = = F2 & A2 F3 A1 F2A1 & A2 F3 F3 =(1-α)F2 + α A2 4/16/ Ardavan Asef-Vaziri

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Exponential Smoothing α=.2 t At Ft F4 =(1-α)F3 + α A3 F4 =.8(110) +.2(120) F4 = = 112 A3 & F3 F4 A1 & A2 F3A1& A2 & A3 F F4 =(1-α)F3 + α A3 Exponential Smoothing Takes into account All pieces of actual data 4/16/ Ardavan Asef-Vaziri

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.2 .05 Smoothing constant The smaller the value of α, the smoother the curve. 4/16/ Ardavan Asef-Vaziri

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Mean Absolute Deviation (MAD) 4/16/ Ardavan Asef-Vaziri The lower the MAD, The better the forecast MAD is also an estimates of the Standard Deviation of forecast 1.25MAD

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Mean Absolute Deviation (MAD) 4/16/ Ardavan Asef-Vaziri

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Tracking Signal UCL LCL Time Detecting non-randomness in errors can be done using Control Charts (UCL and LCL) 4/16/ Ardavan Asef-Vaziri

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Tracking Signal UCL LCL Time Tracking Signal 4/16/ Ardavan Asef-Vaziri

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4/16/2015Ardavan Asef-Vaziri 15 Other Measures of Forecast Error Mean Square Error (MSE) An estimate of the variance of the forecast error Mean absolute percentage error (MAPE)

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4/16/2015Ardavan Asef-Vaziri 16 Measures of Forecast Error

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Forecasts are rarely perfect because of randomness. Beside the average, we also need a measure of variation, which is called standard deviation Forecasts are more accurate for groups of items than for individuals. Forecast accuracy decreases as the time horizon increases. I see that you will get an A this semester. Four Basic Characteristics of Forecasts 4/16/ Ardavan Asef-Vaziri

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