Time series analysis- meaning. Utilities, components and model of time series analysis

Time Series Analysis - Meaning, Utilities, Components and Model of Time Series Analysis

Introduction: A time series is a set of statistical observations arranged is chronological order. Time series may be defined as collection of magnitudes of some variables belonging to different time periods. It is commonly used for forecasting.

Utilities of Time Series Analysis

1. It helps in understanding past behaviour and is useful for prediction of future.

2. It facilitates comparison.

3. The various components of time series are useful to study the effective change under each component.

4. The reasons for variation can be studied by comparing actual with expected results.

 

Components of Time Series

1. Secular Trend: Secular trend is a long term trend which has the basic tendency to grow or decline over a period of time. It may be due to population change technological progress, large scale shifts in consumer tastes, discovery of new things, etc.

2. Seasonal Variation: Seasonal variations are those periodic movements in business activity, which occur regularly every year and have their origin in the nature of the year itself. It may be due to climate weather conditions, customs, traditions and habits, festivals, etc.

3. Cyclical Variation: The term cycle refers to the recurrent variations in time series that usually last longer than a year and are regular neither in amplitude nor in length. Cyclical fluctuations are long-term movements that represent consistently recurring rises and declines in activity. It has four important characteristics:

i. Prosperity

ii. Decline

i. Depression

ii. Improvement

4. Irregular Variation or Irratic Movement: It is the variation in business activities, which do not repeat in a definite pattern. Floods, earthquakes, strikes and wars cause it.

Models of Time Series:

In Traditional time series analysis, it is ordinarily assumed that there is a multiplicative relationship between the components of time series.

Symbolically, Y=T X S X C X I

Where T= Trend

S= Seasonal component

C= Cyclical component

I= Irregular component

Y= Result of four components.

Another approach is to treat each observation of a time series as the sum of these four components

Symbolically, Y=T S C I