The irregular variations are very much erratic in nature. They remain so mixed up with the cyclical variations that are very difficult to separate them from the cyclical variation in a meaningful manner.
However, the following methods may be suggested for identifying and isolating them in a time series, some how or other.
1. Additive Model:
Under this model, the irregular variations are identified by subtracting the sum of the other three components of a time series viz : trend, seasonal and cyclical, from its observed value. Symbolically this given by:
I = Y – (T + S + C)
Where I = irregular variation:
Y = observed value i.e. Yc
T = trend value ,i.e. Yc
S = Seasonal value and
C = Cyclical variation.
2. Multiplicative Method.
Under this model, the irregular variations are measured by dividing the observed values in a time series by the product of its other three components viz : T, S and C. Symbolically, this is given by
Under this method, the irregular variations can, also, be identified in any of the following two forms:
- As ratio of the index of cycle & irregular to the index of the cyclical variation and multiplied by 100 i.e. I =
- As a measure of cyclical normalcy (percentage deviation) by deducting 100 from the index of irregular variation.
Thus, Cyclical normalcy or
Percentage deviation = 1 – 100