Stats Ch 14 Time Series Forecasting- Scripted(2).pptx

Iii exponentially smoothed forecasts use for short

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(iii) Exponentially Smoothed Forecasts Use for short-term forecasting only: i.e. useful if the trend and seasonal component in the data are insignificant F t+1 = Forecast for time t+1 w = exponential smoothing constant Y t = Actual Data at the end of time t F 1 = Y 1 F t+1 = w* Y t + (1-w) F t

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12 Exponential Smoothing Forecasting Example The closing prices on the last day of the month for a stock in 2015-2016 are given in the table . Use w = .2 to forecast the closing price for the January 31, 2017. Exponential Smoothing
13 Exponential Smoothing Forecasting Solution F 1/31/2017 = 55.42 The actual closing price on 1/31/2017 turned out = 62.49. Forecast Error = Y 1/31/2007 F 1/31/2007 = 62.49 – 55.42 = 7.07 F t+1 = w* Y t + (1-w) F t

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14 Measuring Forecast Error
15 Mean Absolute Deviation (MAD) Mean absolute difference between the actual values and forecast of the time series where m = number of forecasts used 1 m t t t Y F MAD m MAD

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17 Mean Absolute Percentage Error Mean of the absolute percentage of the difference between the actual values and forecast of the time series where m = number of forecasts used 1 100 m t t t t Y F Y MAPE m MAPE

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19 Root Mean Squared Error Square root of the mean squared difference between the actual and forecast values of the time series where m = number of forecasts used 2 1 m t t t Y F RMSE m MSE_RMSE

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21 Forecasting Accuracy Example Three time series models were constructed and forecasts made for the next four months. Which more is the most accurate one that should be used for future forecast?

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