ANALYSIS OF TIME SERIES : MEANING and

NEED

RADHAMANI VISHAL & NABHONIL

INTRODUCTIONThe desire to forecast the future is as

old as the human race - older if you allow that animals also form anticipations of what the future may bring, a predator may try to predict where the prey will run…In ancient times, people relied on prophets, soothsayers, and crystal balls. But today we have computers and with them an impressive, ever-expanding array of quantitative capabilities to predict.

WHAT DOES TIME-SERIES MEAN? A time series is a sequence of data points,

measured typically at successive points in time spaced at uniform time intervals.

Time series is a set of measurements of a variable that are ordered through time

Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data

The time series analysis method is quite accurate where future is expected to be similar to past.

DIFFERENCE WITH REGRESSION ANALYSISTime –series Analysis Regression Analysis

Time series forecasting is the use of a model to predict future values based on previously observed values.

Regression analysis is often employed in such a way as to test theories that the current value of one time series affects the current value of another time series.

Regression analysis cannot explain seasonal and cyclical effects.

It shows or suggests

periodicity of a data like seasonal and cyclical effects.

The yearly sunspot numbers between 1749 to 1930 .

A TYPICAL TIME-SERIES GRAPH

COMPONENTS OF TIME SERIES

SECULAR TRENDCYCLICAL VARIATIONSSEASONAL VARIATIONSIRREGULAR VARIATIONS

SECULAR TRENDA time-series which displays a steady tendency of either upward or downward movement in the average (or mean) value of the forecast variable (let us say ‘y’)over a long period of time is called “Trend”. If the values of a variable remain stationery over several

years then we can say that there is no trend in that time series.

Examples-1. Sales of ambassador car is going down over the last few years so ,we can say that sales of ambassador car is showing a “Declining trend”.

2. We find that over the last few years the sales of bike hasincreased. so, we can say that the sales of bike is showing an“Upward Trend”.

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Upward trend of sales of bike in Jamshedpur

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CYCLICAL VARIATIONCyclical variations are long-term movements that representconsistently recurring rises and declines in activity.

for example- Business Cycle, it consists of the recurrence ofthe up and down movements of business activity

depression

revi

val

deflation

inflat

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prosperity

recession

rece

ssion

Prosperity or boom

inflat

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Cyclical Variation(Business cycle)

SEASONAL VARIATIONSeasonal variations are those periodic movements in

business activity which occur regularly every year. Since these variations repeat during a period of

twelve months so, they can be predicted fairly accurately.

for example- Sales of woolen cloths goes up in every winter

season than any other season .The time series graph of sales

of woolen cloths touches its peak in every winter season.We have shown this with the help of a time series graph.

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Sales of woolen cloths in winter season

IRREGULAR VARIATION Irregular variations refer to such variations in

businessactivity which do not repeat in a definite pattern.In these type of variations the pattern of the

variable isunpredictable.

For example- Suppose due to strike by workers of car manufacture company “TOTOYA” in 2012 the production

Of the company went down. The strike here act as a

Irregular factor.

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It has been found that the sales of PEPSI has risen irregularly ,and also the sales of COCA-COLA has taken a deep irregular surge downwards ,as a result of the research which is an unpredictable factor.

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s Sales of PEPSI and COCA-COLA(after research)

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PEPSI

COCA-COLA

CAUSES OF COMPONENTS OF TIME SERIESIf we talk about commodities, Secular Trend is

affected by prices, productions and sales of the commodity as well as the population of the area.

Timing is the most important factor which affect the Cyclical Variations.

Seasonal Variations are caused by climate and weather conditions, customs, festivals and habits.

Irregular Variations are caused by unpredictable factors like natural disasters (earthquakes, floods, wars etc.).These are unpredictable and no one has control over it.

NEED OF TIME-SERIES ANALYSISHelpful in understanding past behaviour By observing data over a period of time, one

can easily understand what changes have taken place in the past, such analysis will be extremely helpful in knowing the past performances.Example- Past exports figures of India can be studied to know the past behaviour of the export trends

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Exp

ort

s (’

00 c

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up

ees)

YEAR EXPORTS(‘00 crore Rupees)

2004-2005 3753.40

2005-2006 4564.18

2006-2007 5717.79

2007-2008 6558.64

2008-2009 8407.55

NEED OF TIME-SERIES ANALYSISHelpful in planning future operations- Knowledge of the past can tell us about the

future .if a trend is repeating over a sufficient long period of time then we can predict for future, so with the help of time series we can predict an unknown value of the series

Example- The time-series graph of profit earned by TATA STEEL LTD. Suggests that ,it has a steady upward trend over the last few years and with the help of last few years data, we can predict more or less its profit for the coming years.

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fits

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Tata

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Ltd

.( c

rore

ru

pees)

years Profits(cr Rs)

2008-09 6000

2009-10 7800

2010-11 9900

2011-12 11000

2012-13 13000

NEED OF TIME-SERIES ANALYSISHelpful in evaluating current

accomplishments- Actual performances can be compared with

the expected performance and the cause of the variations analysed

Example-Accessories firm, Rayban Sunglasses decided to sell 9000 sunglasses in the month of May 2012.But could sell sunglasses to the unit of 8000 only.It was later found that during the month of May ,due to less heat and low temperature, less number of sunglasses were demanded.

Target sales

Actual Sales

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Sales of RAYBAN Sunglasses till June 2012

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Months Units(‘000)

Jan 5

Feb 5.8

Mar 6.5

Apr 7.6

May 8

NEED OF TIME-SERIES ANALYSISFacilitates comparison- Different time-series can be compared and

important conclusions can be drawn from this with the help of this we can take decisions.

Example-Comparative GDP (per capita) growth index of India along with China facilitates users to chart out useful conclusions.

…..And your QUESTIONS begin here!