Forecasting. Successful operations of the company

1.

Forecasting

2.

Successful operations of the company
Effective planning
Accurate forecasting

3.

Forecasting techniques:
Mechanical extrapolation
Simulation
Linear interpolation
Exponential smoothing
Barometric methods
Leading indicators
Compound indexes
Diffuse indexes
Collection of opinions and reviews of goals

4.

Forecasting techniques
Mechanical extrapolation
Originally extrapolation methods are mechanical
and not closely linked to economic theory

5.

However, they are widely used by professional
economists who make forecasting
Because of they are easy to apply and satisfy
reasonably the requirements of the
management

6.

Forecasting techniques:
Mechanical extrapolation
The simplest models:
All future values of the studied
variable in some way are a function of
its present or recent status
]^Y – the experimental value of the analyzed variable
^Y – the predicted value of the analyzed variable
t – index to distinguish periods

7.

Forecasting techniques:
Mechanical extrapolation
The simplest models:
Unchanging model
The predicted value of the variable for the next period will be equal
to its value in the present period
^
Y
t+1
= Y
t
Proportionaly - changing model
The value of a variable changes from current to next period will be
proportional to the value of a variable changes from the previous
period to the current period
^
Y
t+1
= Y t+ k ∆ Y
t
Evaluation of k based on retrospective information.
K = 1 is a uniformly changing the model

8.

Forecasting techniques:
Mechanical extrapolation
The simplest models:
The vast majority of all economic, political and social
decisions are made based on considered the simplest
models
For most short-term predictions the simplest models are
the most easy ways of forecasting, since they are easy to
use and requires minimal information for calculating

9.

TASK: Forcasting based on extrapolation
It is known that in 2008 your company's servers were exposed to
245 DDoS attacks, in 2009 – 315, in 2010 - 298 in 2011 – 306, in 2012379, in 2013 – 376. As a specialist in information security, using the
method of extrapolation on the current average annual growth rate
in the number of attacks, make a forcast about the number of DDoS
attacks on the servers of your company in 2014.

10.

Guidelines for decision :
1. The forecast value of the parameter on the basis of extrapolation in the current
average annual growth rate is determined by the formula
Кn+1 – the forecast value of the parameter;
Кn – parameter value in the reporting period;
Тср.г. – the average annual rate of growth of parameter.
2. The average annual growth rate is an indicator of the intensity changes in the
levels of the series :
Тц1, Тц2,…,Тцn – the parameter of chain growth for periods; n is the number of periods.

11.

3. Chain growth rate is the ratio of each next level of series to previous and calculated
by the formula :
4. The rate of growth, like a chain, and the average, characterize the relative rate of
change of the level of series during the relevant period (or unit time)
Тпр.ц – chain increment rate;
Тц – chain growth rate.
Тпр.ср.г. – chain increment rate;
Тср.г. – среднегодовой темп роста.

12.

Forecasting techniques:
Mechanical extrapolation
Time series analysis:
Time series consist of values corresponding to certain points or
periods
Ordered in time indicators: sales, production volume, prices….

13.

Forecasting techniques:
Mechanical extrapolation
Time series analysis:
Why fluctuation is typical for the time series?
Usually there are four sources of variation in
economic time series, :
1) Trend (T)
2) Seasonal changes (S)
3) Cyclic changes (C)
4) Irregular forces (I)

14.

Forecasting techniques:
Mechanical extrapolation
Time series analysis:
1) Trend (Т)
Is a long-term increase or decrease of series
1) Seasonal changes (S)
Due to weather conditions and habits appear almost at the
same time of a year (for example, New Year, Easter and
other holidays, during which various purchases are made)

15.

Forecasting techniques:
Mechanical extrapolation
Time series analysis:
3) Cyclic changes (С)
Cover periods of several years, reflect the level of economic
boom or recession
4) Irregular forces (I)
Strikes, war. Inconsistent in their effect on individual
series, but, nevertheless, be taken into account

16.

Forecasting techniques:
Mechanical extrapolation
Time series analysis:
Seasonal changes and the method of moving average
Seasonal changes can be taken into account in the forecast using
the seasonal index, which can be calculated by the method of
moving average
Moving average is calculated by summing the values for
each period for some selected period of time and then
dividing the resulting amount by the number of periods

17.

Forecasting techniques:
Mechanical extrapolation
Time series analysis:
Using the data presented in the table, calculate the moving
average and define seasonal index
Volume of sales
quarter
total
Regroup presented data:

18.

Year
quarter
Sales
4-period
moving
average
centralized
moving
average
Seasonal
index
Step 2: Centralized moving average for each
Step
Step 3:
1: Seasonal
Moving average
indexesover
are the
calculated
four periods
by dividing
is
quarter is calculated as the average of each
the
calculated
actual volume
using aof
consistent
sales for the
set of
corresponding
sales for the 4
consecutive pair of 4-period moving averages
quarter
quartersby centralized moving average for the same
period
Step 4: arrange seasonal indexes quarterly
Each subsequent calculation does not include the
first quarter and adds the next quarter

19.

Average value is 1.01: adjust seasonal indices up or down,
revealing trends and maintaining the average value of the four
indexes equal to 1
Step 5: Make normatization: the average value of the four average seasonal
indexes must be equal to 1
Data to calculate Seasonal indexes
Year
total
Average Seasonal index
0,99
1,38
0,98
0,65

20.

Step 6: preparation of the forecast for each quarter of the coming
year: multiply the last centered moving average for the quarter by its
seasonal index
Year
quarter
4-period
moving
average
Sales
Average Seasonal index
0,99
1,38
0,98
0,65
Q1: 316 (для 1989) * 0,99 = 312,84 $
Q2: 322 (для 1989) * 1,38 = 444,36 $
Q3: 307 (для 1988) * 0,98 = 300, 86 $
Q4: 311 (для 1988) * 0,65 = 202,15 $
centralized
moving
average
Seasonal
index

21.

Forecasting techniques:
Mechanical extrapolation
Time series analysis:
Designing of trend
As a forecasting method assumes that
started change in the variable will continue
in the future
The most widely used method of trend
detection is regression analysis, namely the
method of least squares
The method consists of the selection of a
regression line according to the observations
so that the squares of their deviations from
the regression line were minimal

22.

] Y – the observed value of the analyzed variable
^
Y – the predicted value of the analyzed variable
^
Regression line is presented by: Y = a + bt, where a and b parameters of evaluation, t – number of period
To find the values of the
parameters a and b, it is
necessary to solve the
system of equations
Y
na
b
t
tY
a
t
b
t
2

23.

Trend estimates are more reliable if they are based on
data released from seasonal effects
Seasonal effects are smoothed by a moving
average

24.

Y
na
b
t
tY
a
t
b
t
2
Year
centralized moving
Average Y
Period
Y = 284,382 + 1,632 t
total