If you are informed that the price of one kilogram sunflower oil was Rs.0.50 in the year 1940 and in the year 1980 it was Rs. 30 and in the year 2004 it is reported to be Rs. 70, and if you are asked this question: shall sunflower oil be sold again in the future for either Rs.0.50 and Rs. 30 per kg? Surely, you answer would be ‘No’.
Another Question: Shall sunflower oil be sold again in future for Rs.60 per kg? No doubt, you answer would be ‘Yes’. Have you ever thought about how you answered the above two questions? Probably you have not! The analysis of these answers shall lead us to arrive at the following observations:
– There are several causes which affect the variable gradually and permanently. Therefore we are prompted to answer ‘No” for the first question.
– There are several causes which affect the variable for the time being only. For this reason we are prompted to answer ‘Yes’ for the second question.
The causes which affect the variable gradually and permanently are terms as “Long-Term Causes”. The examples of such causes are: increase in the rate of capital formation, technological innovations, the introduction of automation, changes in productivity, improved marketing etc. The effect of long term causes is reflected in the tendency of a behavior, to move in an upward or downward direction, termed as ‘Trend’ or ‘Secular Trend’. It reveals as to how the time series has behaved over the period under study.
The causes which affect the variables for the time being only are labelled as ‘Short-Term Causes”. The short term causes are further divided into two parts, they are ‘Regular’ and ‘Irregular’. Regular causes are further divided into two parts, namely ‘cyclical causes’ and seasonal causes’. The cyclical variations are also termed as business cycle fluctuations, as they influence the variable. A business cycle is composed of prosperity, recession, depression and recovery. The periodic movements from prosperity of recovery and back again to rosperity vary both in time and intensity. The seasonal causes, like weather conditions, business climate and even local customs and ceremonies together play an important role in giving rise to seasonal movements to almost all the business activities. For instance the yearly weather conditions directly affect agricultural production and marketing.
It is worthwhile to say that the seasonal variations analysis will be possible only if the season-wise data are available. This fact must be checked first. For analysing the seasonal effect various methods are available. Among them seasonal index by ‘Ratio to Moving Average Method’ is the most widely used. However, if collected data provides only yearly values, there is no possibility of obtaining seasonal variations. Therefore, the residual amount after eliminating trend will be the effect or irregular or random causes.
Irregular causes are also termed as ‘Erratic’ or ‘Random’ causes. Random variations are caused by infrequent occurrences such as wars, strikes, earthquakes, floods etc. These reasons either go very deep downwards or very high upwards.
The foregoing paragraphs have, in a way, led us to enumerate the components of the time series. The components from the basis for ‘Time Series Analysis’.
Long-term causes : Secular Trend or Trend (T)
Short-term causes Regular : Cyclical (C)
: Seasonal (S)
Irregular or Random : Erratic (I)
