When quantitative data are arranged in the order of their occurrence, the resulting statistical series is called a time series. The quantitative values are usually recorded over equal time interval daily, weekly, monthly, quarterly, half yearly, yearly, or any other time measure. Monthly statistics of Industrial Production in India, Annual birth-rate figures for the entire world, yield on ordinary shares, weekly wholesale price of rice, daily records of tea sales or census data are some of the examples of time series. Each has a common characteristic of recording magnitudes that vary with passage of time.
Time series are influenced by a variety of forces. Some are continuously effective other make themselves felt at recurring time intervals, and still others are non-recurring or random in nature. Therefore, the first task is to break down the data and study each of these influences in isolation. This is known as decomposition of the time series. It enables us to understand fully the nature of the forces at work. We can then analyse their combined interactions. Such a study is known as time-series analysis.
Components of time series
A time series consists of the following four components or elements:
- Basic or Secular or Long-time trend;
- Seasonal variations;
- Business cycles or cyclical movement; and
- Erratic or Irregular fluctuations.
These components provide a basis for the explanation of the past behaviour. They help us to predict the future behaviour. The major tendency of each component or constituent is largely due to casual factors. Therefore a brief description of the components and the causal factors associated with each component should be given before proceeding further.
- Basic or secular or long-time trend: Basic trend underlines the tendency to grow or decline over a period of years. It is the movement that the series would have taken, had there been no seasonal, cyclical or erratic factors. It is the effect of such factors which are more or less constant for a long time or which change very gradually and slowly. Such factors are gradual growth in population, tastes and habits or the effect on industrial output due to improved methods. Increase in production of automobiles and a gradual decrease in production of food grains are examples of increasing and decreasing secular trend.
All basic trends are not of the same nature. Sometimes the predominating tendency will be a constant amount of growth. This type of trend movement takes the form of a straight line when the trend values are plotted on a graph paper. Sometimes the trend will be constant percentage increase or decrease. This type takes the form of a straight line when the trend values are plotted on a semi-logarithmic chart. Other types of trend encountered are “logistic”, “S-curyes”, etc.
Properly recognising and accurately measuring basic trends is one of the most important problems in time series analysis. Trend values are used as the base from which other three movements are measured.
Therefore, any inaccuracy in its measurement may vitiate the entire work. Fortunately, the causal elements controlling trend growth are relatively stable. Trends do not commonly change their nature quickly and without warning. It is therefore reasonable to assume that a representative trend, which has characterized the data for a past period, is prevailing at present, and that it may be projected into the future for a year or so.
- Seasonal Variations: The two principal factors liable for seasonal changes are the climate or weather and customs. Since, the growth of all vegetation depends upon temperature and moisture, agricultural activity is confined largely to warm weather in the temperate zones and to the rainy or post-rainy season in the torried zone (tropical countries or sub-tropical countries like India). Winter and dry season make farming a highly seasonal business. This high irregularity of month to month agricultural production determines largely all harvesting, marketing, canning, preserving, storing, financing, and pricing of farm products. Manufacturers, bankers and merchants who deal with farmers find their business taking on the same seasonal pattern which characterise the agriculture of their area.
The second cause of seasonal variation is custom, education or tradition. Such traditional days as Dewali, Christmas. Id etc., product marked variations in business activity, travel, sales, gifts, finance, accident, and vacationing.
The successful operation of any business requires that its seasonal variations be known, measured and exploited fully. Frequently, the purchase of seasonal item is made from six months to a year in advance. Departments with opposite seasonal changes are frequently combined in the same firm to avoid dull seasons and to keep sales or production up during the entire year. Seasonal variations are measured as a percentage of the trend rather than in absolute quantities. The seasonal index for any month (week, quarter etc.) may be defined as the ratio of the normally expected value (excluding the business cycle and erratic movements) to the corresponding trend value. When cyclical movement and erratic fluctuations are absent in a lime series, such a series is called normal. Normal values thus are consisting of trend and seasonal components. Thus when normal values are divided by the corresponding trend values, we obtain seasonal component of time series.
3. Business Cycle: Because of the persistent tendency for business to prosper, decline, stagnate recover; and prosper again, the third characteristic movement in economic time series is called the business cycle. The business cycle does not recur regularly like seasonal movement, but moves in response to causes which develop intermittently out of complex combinations of economic and other considerations. When the business of a country or a community is above or below normal, the excess deficiency is usually attributed to the business cycle. Its measurement becomes a process of contrast occurrences with a normal estimate arrived at by combining the calculated trend and seasonal movements. The measurement of the variations from normal may be made in terms of actual quantities or it may be made in such terms as percentage deviations, which is generally more satisfactory method as it places the measure of cyclical
tendencies on comparable base throughout the entire period under analysis.
4. Erratic or Irregular Component: These movements are exceedingly difficult to dissociate quantitatively from the business cycle. Their causes are such irregular and unpredictable happenings such as wars, droughts, floods, fires, pestilence, fads and fashions which operate as spurs or deterrents upon the progress of the cycle. Examples such movements are : high activity in middle forties due to erratic effects of 2nd world war, depression of thirties throughout the world, export boom associated with Korean War in 1950.
The common denominator of every random factor it that does not come about as a result of the ordinary operation of the business system and does not recur in any meaningful manner.
Mathematical Statement of the Composition of Time Series
A time series may not be affected by all type of variations. Some of these type of variations may affect a few time series, while the other series may be effected by all of them. Hence, in analysing time series, these effects are isolated. In classical time series analysis it is assumed that any given observation is made up of trend, seasonal, cyclical and irregular movements and these four components have multiplicative relationship.
O = T × S × C × I
where O refers to original data,
T refers to trend.
S refers to seasonal variations,
C refers to cyclical variations and
I refers lo irregular variations.
This is the most commonly used model in the decomposition of time series.
There is another model called Additive model in which a particular observation in a time series is the sum of these four components.
O = T + S + C + I