Page 247 - DCOM203_DMGT204_QUANTITATIVE_TECHNIQUES_I

Quantitative Techniques – I

Notes
Deseasonalisation of Data
Notes

The deseasonalisation of data implies the removal of the effect of seasonal variations from
the time series variable. If Y consists of the sum of various components, then for its
deseasonalisation, we subtract seasonal variations from it. Similarly, in case of
multiplicative model, the deseasonalisation is done by taking the ratio of Y value to the
corresponding seasonal index. A clue to this is provided by the fact that the sum of seasonal
indices is equal to zero for an additive model while their sum is 400 or 1200 for a
multiplicative model.
It may be pointed out here that the deseasonalisation of a data is done under the assumption
that the pattern of seasonal variations, computed on the basis of past data, is similar to the
pattern of seasonal variations in the year of deseasonalisation.

Did u know? Ratio to moving average method is most general and, therefore, most popular
method of measuring seasonal variations.

Self Assessment

Multiple Choice Questions:

14. If the time series data are in terms of annual figures, the seasonal variations are.....................
(a) Present (b) Absent
(c) In fixed ratio (d) transitory

15. The seasonal variations are of ............................. nature with period equal to one year.
(a) Linear (b) Cyclic
(c) Periodic (d) Varying
16. The measurement of seasonal variation is done by .................................. them from other
components of a time series.
(a) Separating (b) Dissociating
(c) Isolating (d) Filtering
State whether the following statements are true or false:

17. Method of Simple Averages is used when the time series variable consists of only the
seasonal and random components.
18. Ratio to Trend Method is used when cyclical variations are absent from the data.

19. Link Relatives Method is based on the assumption that the trend is linear and cyclical
variations are of uniform pattern.
20. Seasonalisation is the process to eliminate the seasonal variations from the data.

242 LOVELY PROFESSIONAL UNIVERSITY