analysis of time series
DESCRIPTION
a total description of time series analysis with vivid examplesTRANSCRIPT
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.
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”.
Un
its
years
Upward trend of sales of bike in Jamshedpur
2000
2001
2002
2003
2004
2005 2006
2007
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10000
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
ion
prosperity
recession
rece
ssion
Prosperity or boom
inflat
ion
Eco
nom
ic a
ctiv
itie
s
time
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.
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.
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.
2011 2012 2013
2
4
6
8
10
12
14
sale
s Sales of PEPSI and COCA-COLA(after research)
years
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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10000
2004-05 2005-062006-072007-082008-09
Exp
ort
s (’
00 c
rore
R
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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14000
2008-092009-102010-112011-122012-13
Pro
fits
earn
ed
by
Tata
Ste
el
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
5
6
7
8
9
10
Un
its
( in
th
ou
san
ds)
Jan Feb Mar Apr May
Sales of RAYBAN Sunglasses till June 2012
Jun
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.