Page 232 - DCOM203_DMGT204_QUANTITATIVE_TECHNIQUES_I

Unit 11: Analysis of Time Series

(ii) Customs and Traditions: The customs and traditions of the people also give Notes
rise to the seasonal variations in time series. For example, the sale of garments
and ornaments may be highest during the marriage season, sale of sweets
during Diwali, etc., are variations that are the results of customs and traditions
of the people.
It should be noted here that both of the causes, mentioned above, occur regularly
and are often repeated after a gap of less than or equal to one year.

Objectives of Measuring Seasonal Variations
The main objectives of measuring seasonal variations are:
(i) To analyse the past seasonal variations.
(ii) To predict the value of a seasonal variation which could be helpful in short-
term planning.
(iii) To eliminate the effect of seasonal variations from the data.
(b) Causes of Cyclical Variations: Cyclical variations are revealed by most of the economic
and business time series and, therefore, are also termed as trade (or business) cycles.
Any trade cycle has four phases which are respectively known as boom, recession,
depression and recovery phases. These phases are shown in Figure 11.1. Various
phases repeat themselves regularly one after another in the given sequence. The
time interval between two identical phases is known as the period of cyclical
variations. The period is always greater than one year. Normally, the period of
cyclical variations lies between 3 to 10 years.
Objectives of Measuring Cyclical Variations
The main objectives of measuring cyclical variations are:

(i) To analyse the behaviour of cyclical variations in the past.
(ii) To predict the effect of cyclical variations so as to provide guidelines for
future business policies.

3. Random or Irregular Variations: As the name suggests, these variations do not reveal any
regular pattern of movements. These variations are caused by random factors such as
strikes, floods, fire, war, famines, etc. Random variations is that component of a time
series which cannot be explained in terms of any of the components discussed so far. This
component is obtained as a residue after the elimination of trend, seasonal and cyclical
components and hence is often termed as residual component.
Random variations are usually short-term variations but sometimes their effect may be so
intense that the value of trend may get permanently affected.

11.1.3 Analysis of Time Series

As mentioned earlier, the purpose of analysis of a time series is to decompose Y into various
t
components. However, before doing this, we have to make certain assumptions regarding the
manner in which these components have combined themselves to give the value Y . Very often
t
it is assumed that Y is given by either the summation or the multiplication of various
t
components, and accordingly we shall assume two type of models, i.e., additive model or
multiplicative model.

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