Forecasting. Successful operations of the company

indicators
Compound indexes
Diffuse indexes
Collection of opinions and reviews of goals

economic theory

of they are easy to apply and satisfy reasonably the requirements of the management

some way are a function of its present or recent status

^

Forecasting techniques:

] Y – the experimental value of the analyzed variable
Y – the predicted value of the analyzed variable
t – index to distinguish periods

^

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

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

Mechanical extrapolation

Forecasting techniques:

The simplest models:

company's servers were exposed to 245 DDoS attacks, in 2009 – 315, in 2010 - 298 in 2011 – 306, in 2012- 379, 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.

intensity changes in the levels of the series :

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.

Тц1, Тц2,…,Тцn – the parameter of chain growth for periods; n is the number of periods.

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.

or periods

Ordered in time indicators: sales, production volume, prices….

Mechanical extrapolation

Forecasting techniques:

sources of variation in economic time series, :
Trend (T)
Seasonal changes (S)
Cyclic changes (C)
Irregular forces (I)

Time series analysis:

Mechanical extrapolation

Forecasting techniques:

(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)

Time series analysis:

Mechanical extrapolation

Forecasting techniques:

of economic boom or recession

Irregular forces (I)

Strikes, war. Inconsistent in their effect on individual series, but, nevertheless, be taken into account

Time series analysis:

Mechanical extrapolation

Forecasting techniques:

by summing the values for each period for some selected period of time and then dividing the resulting amount by the number of periods

Seasonal changes can be taken into account in the forecast using the seasonal index, which can be calculated by the method of moving average

Time series analysis:

Mechanical extrapolation

Forecasting techniques:

the table, calculate the moving average and define seasonal index

Volume of sales

quarter

total

a consistent set of sales for the 4 quarters

Each subsequent calculation does not include the first quarter and adds the next quarter

Step 2: Centralized moving average for each quarter is calculated as the average of each consecutive pair of 4-period moving averages

Step 3: Seasonal indexes are calculated by dividing the actual volume of sales for the corresponding quarter by centralized moving average for the same period

Step 4: arrange seasonal indexes quarterly

quarter

Year
Sales

4-period
moving
average

centralized
moving
average

Seasonal
index

seasonal indexes must be equal to 1

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

0,99 1,38 0,98 0,65

Year

Average Seasonal index

total

Data to calculate Seasonal indexes

1989) * 1,38 = 444,36 $
Q3: 307 (для 1988) * 0,98 = 300, 86 $
Q4: 311 (для 1988) * 0,65 = 202,15 $

Average Seasonal index
0,99 1,38 0,98 0,65

4-period
moving
average

centralized
moving
average

Seasonal
index
Sales

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

quarter

Year

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

Time series analysis:

Mechanical extrapolation

Forecasting techniques:

– 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

released from seasonal effects

Seasonal effects are smoothed by a moving average