Meaning, Uses and Basic Components of Time Series Analysis

Meaning, Uses and Basic Components

Meaning:

A time series consists of a set of observations arranged in chronological order (either ascending or descending). Time Series has an important objective to identify the variations and try to eliminate the variations and also helps us to estimate or predict the future values.

Why should we learn Time Series?

It helps in the analysis of the past behavior.

It helps in forecasting and for future plans.

It helps in the evaluation of current achievements.

It helps in making comparative studies between one time period and others.

Therefore time series helps us to study and analyze the time related data which involves in business fields, economics, industries, etc…

Components of Time Series

There are four types of components in a time series. They are as follows;

(i) Secular Trend

(ii) Seasonal variations

(iii) Cyclic variations

(iv) Irregular variations

(i) Secular Trend

It is a general tendency of time series to increase or decrease or stagnates during a long period of time. An upward tendency is usually observed in population of a country, production, sales, prices in industries, income of individuals etc., A downward tendency is observed in deaths, epidemics, prices of electronic gadgets, water sources, mortality rate etc…. It is not necessarily that the increase or decrease should be in the same direction throughout the given period of time.

(ii) Seasonal Variations

As the name suggests, tendency movements are due to nature which repeat themselves periodically in every seasons. These variations repeat themselves in less than one year time. It is measured in an interval of time. Seasonal variations may be influenced by natural force, social customs and traditions. These variations are the results of such factors which uniformly and regularly rise and fall in the magnitude. For example, selling of umbrellas’ and raincoat in the rainy season, sales of cool drinks in summer season, crackers in Deepawali season, purchase of dresses in a festival season, sugarcane in Pongal season.

(iii) Cyclic Variations

These variations are not necessarily uniformly periodic in nature. That is, they may or may not follow exactly similar patterns after equal intervals of time. Generally one cyclic period ranges from 7 to 9 years and there is no hard and fast rule in the fixation of years for a cyclic period. For example, every business cycle has a Start- Boom- Depression-Recover, maintenance during booms and depressions, changes in government monetary policies, changes in interest rates.

(iv) Irregular Variations

These variations do not have particular pattern and there is no regular period of time of their occurrences. These are accidently changes which are purely random or unpredictable. Normally they are short-term variations, but its occurrence sometimes has its effect so intense that they may give rise to new cyclic or other movements of variations. For example floods, wars, earthquakes, Tsunami, strikes, lockouts etc…

Mathematical Model for a Time Series

There are two common models used for decomposition of a time series into its components, namely additive and multiplicative model.

(i) Additive Model:

This model assumes that the observed value is the sum of all the four components of time series. (i.e) Y= T+S+C+I

where          Y = Original value , T = Trend Value , S = Seasonal component

C = Cyclic component  , I = Irregular component

The additive model assumes that all the four components operate independently. It also assumes that the behavior of components is of an additive character.

(ii) Multiplicative Model:

This model assumes that the observed value is obtained by multiplying the trend(T) by the rates of other three components. Y = T × S × C × I

where          Y = Original value , T = Trend Value , S = Seasonal component

C = Cyclic component  , I = Irregular component

This model assumes that the components due to different causes are not necessarily independent and they can affect one another. It also assumes that the behavior of components is of a multiplicative character.