There are two kinds of short term variations in a time series – seasonal variations and cyclical variations. Seasonal variations in a time series are due to the rhythmic forces which operate in a regular and periodic manner over a span of less than a year, i.e., during a period of 12 months and have the same or almost the same pattern year after year. Thus, seasonal variations in a time series will be there if the data are recorded quarterly (every three months), monthly, weekly, daily, hourly, and so on.
Although in each of the above cases, the amplitudes of the seasonal variations are different, all of them have the same period, viz., 1 year. Thus in a time series data where only annual figures are given, there are no seasonal variations. Most of the economic time series are influenced by seasonal swings, e.g., prices, production, and consumption of commodities; sales and profits in a departmental store; bank clearings and bank deposits, etc., are all affected by seasonal variations.
Causes behind Seasonal Variation:
- Those resulting from natural forces – As the name suggests, the various seasons or weather conditions and climatic changes play an important role in seasonal movements. For instance, the sales of umbrellas pick up very fast in the rainy season; the demand for electric fans goes up in the summer season; the sales of ice and ice cream increase very much in summer; the sales of woolens go up in winter – all being affected by natural forces, viz., weather or seasons. Likewise, the production of certain commodities such as sugar, rice, pulses, eggs, etc., depends on seasons. Similarly, the prices of agricultural commodities always go down at the time of harvest and then pick up gradually.
- Those resulting from man-made conventions – These variations in a time series within a period of 12 months are due to habits, fashions, customs, and conventions of the people in the society. For instance, the sales of jewelry and ornaments go up during marriages; the sales and profits in departmental stores go up considerably during marriages, and festivals like Christmas, etc. Such variations operate in a regular manner and recur year after year.
The main objective of the measurement of seasonal variations is to isolate them from the trend and study their effects. A study of the seasonal patterns is extremely useful to businessmen, producers, sales managers, etc., in planning future operations and in the formulation of policy decisions regarding the purchase, production, inventory control, personnel requirements, and selling and advertising programs. In the absence of any knowledge of seasonal variations, a seasonal upswing may be mistaken as an indicator of better business conditions while a seasonal slump may be misinterpreted as deteriorating business conditions. Thus, to understand the behavior of the phenomenon in a time series properly, the time series data must be adjusted for seasonal variations.
Reference: Business Statistics – S.C Gupta, Indra Gupta
