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Methods of forecasting

Methods of forecasting

There is nothing new about business forecasting as, for centuries, the businessmen have tried to adjust themselves in such a manner as to make the best out of the future conditions. The rule-of-thumb method has been widely practiced in business. It consists of deciding about the future in terms of past experience and familiarity with the problem at hand even today. This method is very widely used in business however; it can lead to absurd conclusions if employed by the inexperienced.

A forecast us usually a combination of several technique.

**1. ****Business barometers:** - of great assistance in practical forecasting is a series that can be used index or indicator is also widely, though loosely, used in business statistics; sometimes the term is used to mean simply an indicator of the present economic situations and sometimes it is used to designate an indicator of future conditions.

**The following are some of the important series which aid businessmen in forecasting:**

**1. **Gross national product

**2. **Employment

**3. **Wholesale prices

**4. **Consumer prices

**5. **Industrial production

**6. **Volume of bank deposits and currency outstanding

**7. **Consumer credit

**8. **Disposable personal income

**9. **Departmental store sales

**10. **Stock process

**11. **Bond yields.

**2. ****Extrapolation:-** extrapolation is the simplest yet often a useful method of forecasting. In many forecasting situation the most reasonable expectation is that the variable will follow its already established path. Extrapolation relies on the relative constancy in the pattern of past movements in some time series. Strictly speaking, nothing needs to be known about causation-why the series moves as it does. But in practice the justification does involve the nature of the growth process being described. Extrapolation is used frequently for sales forecasts and for other estimates when better forecasting methods may not be justified.

Most of the most useful ones are:

**(a) ****Arithmetic trend:-** the straight-line arithmetic trend assumes that growth will be b a constant absolute amount each year.

**(b) ****Semi-log trend:-** the semi-logarithmic trend assumes a constant percentage increase each year. Since the annual increment is constant in logarithms. This line translates into a straight line when drawn on paper within a logarithmic vertical scale.

**(c) ****Modified exponential trend:-** this curve assumes that each increment of growth will be a constant per cent than 100 of the previous one. The line terns generally to approach, by never quite reach a distant asymptote, which may be thought of as an upper limit.

**3. ****Regression analysis:- **the regression approach offers many valuable contributions to the solution of the forecasting problem. It is the means by which we select form among the many possible or theoretically suggestive relationships between to variables to precise quantified knowledge. If possible an estimate of the other. For example. If we know that advertising expenditure and sales are correlated then for a given advertising expenditure. We can find out the probable increase in sales or vice versa.

**4. ****Econometric models:**- econometric techniques which originated in the eighteenth century, have recently gained in popularity for forecasting. Much or the revival of econometrics is attributed to the growth of computer technology. The term econometrics refers to the application of mathematical economic theory and statistical procedures to economic data in order to verify economic theorems and to establish quantitative results in economics. An all in the econometric models take the form of a set of simultaneous equations. The values of the constants in such equations are supplied by a study of statistical time series and a large number of equations may be necessary to produce an adequate model. The work of computations is greatly facilitated by electronic data processing equipment like computer etc.

**5. ****Forecasting by the use of time series analysis:**- time series analysis helps to identify and explain:

**1. **Any regular or systematic variation in the series of data which is due to seasonality-the seasonal.

**2. **Cyclical patterns.

**3. **Trends in the data.

**4. **Growth rates of these trends unfortunately; most existing methods indentify only the seasonal, the combined effect of trends and excises separate trend from cycles.

6. **Opinion polling:-** opinion poll is the survey of opinion of experts knowledgeable persons in the field whose views carry lot of weight, for example, a survey of opinion of sales representatives, wholesalers, retailers, etc. shall be of great help in formulating demand projections. The survey research centre of the University of Michigan conducts an annual pool regarding the future plans of consumers. The answers too many questions are translated into short-run demand for color television sets, automobiles and other consumer products.

7. **Causal models: -** a causal model is the most sophisticated king of forecasting tool. It expresses mathematic call the relevant causal relationships. And may include pipeline considerations (inventories) and market survey information. It may also directly incorporate the result fo a time series analysis.

**Illustration:** from the following values prepare forecasts by the methods of exponential smoothing taking initial estimate as 100, the values of α = 0.04 and an initial trend value zero:

Time period (f) |
1999 |
2000 |
2001 |
2002 |
2003 |
2004 |
2005 |
2006 |
2007 |
2008 |

Sales ($ crores) |
104 |
108 |
118 |
115 |
120 |
122 |
123 |
125 |
128 |
130 |

Solution:

t |
X1 |
Smoothed value St |
Change in St ?St |
Trend estimate t |
Forecast St |
Forecasting error (e) |

1999 |
104 |
100.0 |
- |
- |
- |
- |

2000 |
108 |
103.2 |
3.20 |
1.28 |
105.12 |
-12.88 |

2001 |
118 |
109.12 |
5.92 |
3.14 |
113.83 |
-1.17 |

2002 |
115 |
111.47 |
2.35 |
2.82 |
115.70 |
-4.30 |

2003 |
120 |
114.88 |
3.41 |
3.06 |
119.47 |
-2.53 |

2004 |
122 |
117.73 |
2.85 |
2.98 |
122.20 |
-0.80 |

2005 |
123 |
119.84 |
2.11 |
2.63 |
123.79 |
-1.21 |

2006 |
125 |
121.90 |
2.06 |
2.40 |
125.50 |
-2.50 |

2007 |
128 |
124.34 |
2.44 |
2.42 |
127.97 |
-2.03 |

2008 |
130 |
126.60 |
2.26 |
2.36 |
130.14 |
- |

8. **Exponential smoothing:-** this method is an outgrowth of the recent attempts to maintain the smoothing function of moving averages without their corresponding drawbacks and limitations. Exponential smoothing is a special kind of weighted average and is found extremely useful in short-term forecasting of inventories and sales.

Smoothing process: - the steps in smoothing process are:

**1. **The exponentially smoothed value at time period t is denoted by S_{i} the smoothing process begins by assigning **S _{1} = X_{1}** at the first time period. For the second time period.

**S _{2} = a X_{2} + β S_{1}**

And for any succeeding time period t the smoothed value S_{i} is found by computing.

**S _{t} = a X_{t} + β S_{t} - 1 **

9. **Survey method; -** the survey method is very widely iced as a tool of forecasting for the existing any new products. Field surveys are conducted and the necessary information, both quantitative and qualitative obtained. Forecasts are made out about likely demand expenditure on consumer durables. Etc. attitude of consumers about consumption of different items provides very useful information.

**Cautions while using forecasting techniques**

Forecasting business conditions is a complex task which cannot be accomplished with exactness. The economic social and political forces which shape the future are many and varied. Their relative importance changes almost constantly. It is obvious therefore, that statistical methods cannot claim to be able to make the uncertain future certain. It does not follow from this disclaimer that statistical methods have mouthing to contribute to business forecasting. The question is not between forecasting and not forecasting instead it is what kind of forecast? Lack of a forecast implies a dangerous type of forecast, the mere warning of a possibility of a change is better than no warning at all as is wisely said forewarned is forearmed also is should be remembered the forecasts are not made just for the sake of forecasting that is they are not ends in themselves, forecasts are made in order to assist management determine a strategy and alternative strategies.

It may be pointed out that forecasting is much more than projecting a series mechanically. Though future is some sort of extension of the past, but it can change are numerous and complex. They are often difficult to discover and to measure. They may appear in all kinds of combinations and may be constantly changing. The fact that past can never be a perfect guide to the future warns techniques or theoretical ideas to a list of unchanging variables; successful forecasting requires expert blending or economic theory with significant statistical expertise and thorough familiarity with the relevant statistical data. Both qualitative and quantitative information must be utilised.

As a final word of cautions, it may be emphasized that no matter what methods of forecasting are used, it is essential that the forecasts be casket by the judgment, of statistical data is an attempt to substitute facts for subjective in a given situation should be ignored in favour of quantitative data. It is particularly important to take into consideration any specific plans of the business that might affect the pattern of sales in relation to indicators used of forecasting. More successful forecasting will result by combining with statistical forecasting the judgment and knowledge of current business trends,

Also it is impor5tant to emphasize that any forecast shoulder be reviewed frequently and revised in to light of the most recent information. Forecasting is not a one-and operations. To be effective, it requires continuous attention, a unantici pated elopement will of exchange our picture of the future, or at least clarifies. In term of an original decisions and actions that have been taken this rule implies continuous modification wherever necessary, the technique of flexible budgets has been developed to permit the revision of the budget estimates and everyone dealing with forecasts should be alert to the need for constantly checking to see if anything ahs happened to change the outlook keeping accurately informed about the current level of business is probably the simplest insure ace that can be secured against making wrong decisions regarding the future.

Despite all advances that have been made in the techniques of forecasting remains more an art than a science. The value of a forecast lies not merely in its accuracy, but the fact that making it requires a balanced consideration of factors influencing future developments, right or wrong, further forecasting should not be regarded merely as a means of peering into the future and then accepting what one sees; it needs to be used actively as a way of guiding the firm along the path its management feels is most desirable business forecasting what one sees: it needs to be used actively as a way of guiding the firm along the path its management feels is most desirable. Business forecasting will not only help in the short-term control of operations its greatest contribution probably will come when it is able to improve short and ling term corporate strategies.

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