Page 222 - DMGT404 RESEARCH_METHODOLOGY

Research Methodology

Notes Double Exponential Smoothing (Trend-Adjusted Exponential Smoothing)
When a trend exists, the forecasting technique must consider the trend as well as the series
average ignoring the trend will cause the forecast to always be below (with an increasing trend)
or above (with a decreasing trend) actual demand double exponential smoothing smooths
(averages) both the series average and the trend

forecast for period t + 1: Ft + 1 = At + Tt
average: At = aDt + (1 – a) (At – 1 + Tt – 1) = aDt + (1 – a) Ft
average trend: Tt = B CTt + (1 – B) Tt - 1
current trend: CTt = At – At – 1

forecast for p periods into the future: Ft + p = At + pTt
where:
At = exponentially smoothed average of the series in period t
Tt = exponentially smoothed average of the trend in period t

CTt = current estimate of the trend in period t
a = smoothing parameter between 0 and 1 for smoothing the averages
B = smoothing parameter between 0 and 1 for smoothing the trend

Self Assessment

Fill in the blanks:

7. Time series forecasting methods are based on analysis of ……………data.
8. …………………..gives greater weight to demand in more recent periods.
9. …………………..method is based on the principle that the total effect of periodic variations
at different points of time in its cycle gets completely neutralised.
10.4 The Mean Absolute Deviation (MAD)

The mean absolute deviation is the sum of the absolute values of the deviations from the mean.
Procedure
1. Find the mean of the data

2. Subtract the mean from each data value to get the deviation from the mean
3. Take the absolute value of each deviation from the mean
4. Total the absolute values of the deviations from the mean
5. Divide the total by the sample size.

Typically the point from which the deviation is measured is the value of either the median or the
mean of the data set.
|D| = |x – m(X)|
i

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