Forecasting techniques, time series analysis
Download as PPTX, PDF1 like1,354 views
This document discusses forecasting techniques and time series analysis. It defines forecasting as the estimation or prediction of future outcomes, trends, or behavior through the use of statistics. The document outlines several key points: - It describes the meaning, definition, features, process, importance, advantages, and limitations of forecasting. - It discusses various qualitative and quantitative forecasting methods including regression analysis, business barometers, input/output analysis, surveys, and time series analysis. - It explains the components of time series analysis including secular trends, seasonal variations, cyclical variations, and irregular variations. - It provides examples of each type of variation and discusses their importance for time series forecasting.
Forecasting techniques, time series analysis
- 1. FORECASTING TECHNIQUES, & TIME SERIES ANALYSIS Satish Kumar (21n78e@gmail.com) Architect ,Urban Planner ,Researcher, G.C.A ,Lucknow, Jamia Millia Islamia-Delhi, G..B.U-Greater Noida
- 2. CONTENT OF PRESENTATION • Introduction • Meaning • Definition • Features of forecasting • Process of forecasting • Importance of forecasting • Advantages of forecasting • Limitations of forecasting • Methods of forecasting • Conclusion
- 3. • In simple terms forecasting means, “estimation or prediction of future”. • The prediction of outcomes, trends, or expected future behaviour of a business, industry sector, or the economy through the use of statistics. • Forecasting is an operational research technique used as a basis for management planning and decision making. INTRODUCTION
- 4. • Forecasting is a systematic guessing of the future course of events. • Forecasting provides a basis for a planning. • According to Fayol, forecasting includes both assessing the future and making provision for it. Definition Websters new collegiate dictionary defines that, “A forecast is a prediction and its purpose is to calculate and predict some future events or condition.” Allen L.A., “forecasting is a systemic attempt to probe the future by inference from known facts.” Neter & Wasserman, “business forecasting is refers to a statistical analysis of the past and current movements in the given time series so as to obtain clues about the future pattern of these movement. MEANING & DEFINITION
- 5. • It is concerned with future events. • It is necessary for planning process. • The impact of future events has to be considered in the planning process. • It is a guessing of future events. • It considers all the factors which affect organizational functions. • Personal observation also helps forecasting. FEATURES IN FORECASTING
- 6. 1. Thorough preparation of foundation: The very purpose of thorough preparation of a foundation is that the forecasting is based on the foundation. 2. Estimation of future: The brightness of future period can be estimated in consultation with the key personnel & it may be communicated to all the employees of the business 3. Collection of results: Relevant records are prepared & maintained to collect the result. 4. Comparison of results: The actual results are compared with estimated results to know deviations. 5. This will help the management to estimate the future. 6. Refining the forecast: The forecast can be refined in the light of deviations which seem to be more realistic. PROCESS OF FORECASTING
- 7. 1. Pivotal role in an organization:- Many organizations have failed because of lack of forecasting or faulty forecasting. The reason is that planning is based on accurate forecasting. 2. Development of a business:- The performance of specified objectives depends upon the proper forecasting. So the development of a business or an organization is fully based on the forecasting. 3. Co-ordination:- Forecasting helps to collect the information about internal and external factors. Thus collected information provides a basis for co-ordination. 4. Effective control:- Management executive can ascertain the strength and weaknesses of sub-ordinates or employees through forecasting. 5. Key to success:- All business organizations are facing risks. Forecasting provides clues and reduce risk and uncertainties. The management executives can save the business and get success by taking appropriate IMPORTANCE OF FORECASTING
- 8. 6. Implementation of project:- Many entrepreneurs implement a project on the basis of their experience .Forecasting helps an entrepreneur to gain experience and ensures him success. 7. Primacy to planning:- The information required for planning is supplied by forecasting. So, forecasting is the primacy to the planning IMPORTANCE OF FORECASTING
- 9. • Effective handling of uncertainty • Better labor relations • Balanced work-load • Minimization in the fluctuations of production • Better use of production facilities • Better material management • Better customer service • Better utilization of capital and resources • Better design of facilities and production system. ADVANTAGES OF FORECASTING
- 10. • Forecasting is to be made on the basis of certain assumptions and human judgments which yield wrong result. • It can not be considered as a scientific method for guessing future events. • It does not specify any concrete relationship between past and future events. • It requires high degree of skill. • It needs adequate reliable information so difficult to collect reliable information. • Heavy cost and time consuming. • It can not be applied to a long period.. LIMITATIONS OF FORECASTING
- 11. METHODS OF FORECASTING methods qualitative quantitative • There are mainly two methods of forecasting. • These methods can be sub classified into further categories
- 12. 1.Regression analysis • Regression is concerned with obtaining a mathematical equation which describes the relationship between two variables. 1. The independent variable is the one that is chosen freely or occurs naturally. 2. The dependent variable occurs as a consequence of the value of the independent variable. • It is normally used for estimation purposes. Simple Linear Regression • Analysis of single regressor • One independent variable explains the behavior of dependent variable Multiple Regression • Application with more than one regressor • More than one independent variable explains the behavior of dependent variable Simple Regression Analysis.
- 13. 1.Regression analysis • Type of regression models • For example , if we take two inter related variables viz. cost of production and profit ,there will be a direct relationship prevailing between this two variables. • It is possible to have an estimate of profit on the basis of cost of production ,provided other things remain the same.
- 14. METHODS 2. Business barometer:- • Index numbers are used to measure the state of condition of business condition between two or more periods. • Business trend, seasonal fluctuations of a business and cyclical movements are studied with the help of index numbers. 3. Input and output analysis:- • Under this method, a forecast can be made if the relationship between input and output is known . • At the same time , the input requirements can be forecast of the basis of output. • In other words, input can be determined on the basis of need of output.
- 15. METHODS 4. Survey method:- • Field survey can be conducted to collect information regarding the attitude of people. 5. Time series analysis :- • This method is quite accurate where future is expected to be similar to the past. • Time series analysis can be applied. Only when the data are available for a long period of time.
- 16. METHODS 6. Delphi method:- 1. Rand corporation has developed the Delphi method initially in 1969 to forecast the military events. 2. Then, it has been applied in other areas also. Delphi method is useful when past data are not available and where the past data don’t give an indication for the future 3. A systematic forecasting method that involves structured interaction among a group of experts on a subject. An organized method for collecting views and information pertaining to a specific area. Gathering a group of experts to forecast events and assess complex issues.
- 17. METHODS How to use it:- Process:-
- 18. TIME SERIES ANALYSIS Definition • “A time series is a set of observation taken at specified times, usually at equal intervals”. • Time series establish relation between “cause” & “Effects”. • One variable is “Time” which is independent variable & and the second is “Data” which is the dependent variable
- 19. EXAMPLE • From example 1 it is clear that the sale of milk packets is decrease from Monday to Friday then again its start to increase. • Same thing in example 2 the population is continuously increase
- 20. IMPORTANCE OF TIME SERIES ANALYSIS • As the basis of Time series Analysis businessman can predict about the changes in economy. There are following points which clear about the its importance: 1. Profit of experience. 2. Safety from future 3. Utility Studies 4. Sales Forecasting 5. Budgetary Analysis 6. Stock Market Analysis 7. Yield Projections 8. Process and Quality Control 9. Inventory Studies 10. Economic Forecasting 11. Risk Analysis & Evaluation of changes. 12. Census Analysis
- 21. COMPONENTS OF TIME SERIES ANALYSIS • The change which are being in time series, They are effected by Economic, Social, Natural, Industrial & Political Reasons. These reasons are called components of Time Series. • Secular trend :The increase or decrease in the movements of a time series is called Secular trend. • Seasonal variation :Seasonal variation are short-term fluctuation in a time series which occur periodically in a year. This continues to repeat year after year. • Cyclical variation :Cyclical variations are recurrent upward or downward movements in a time series but the period of cycle is greater than a year. Also these variations are not regular as seasonal variation • Irregular variation :Irregular variations are fluctuations in time series that are short in duration, erratic in nature and follow no regularity in the occurrence pattern. These variations are also referred to as residual variations since by definition they represent what is left out in a time series after trend ,cyclical and seasonal variations.
- 22. 1. SECULAR TREND • Secular trend : • The increase or decrease in the movements of a time series is called Secular trend. • The secular trend refers to the general tendency of data to grow or decline over a long period of time • Eg: • The population of India over years shows a definite rising tendency. • The death rate in the country after independence shows a falling tendency because of advancement of literacy and medical facilities. • Here long period of time does not mean as several years. • Whether a particular period can be regarded as long period or not in the study of secular trend depends upon the nature of data.
- 23. 2. SEASIONAL TREND • Seasonal variation : • Seasonal variation are short-term fluctuation in a time series which occur periodically in a year. This continues to repeat year after year. • Here the period of time may be monthly, weekly or hourly. • There occur seasonal fluctuations in a time series due to two factors. Due to natural forces Manmadeconvention. For example • During winter there is greater demand for woolen clothes, hot drinks etc. Where as in summer cotton clothes, cold drinks have a greater sale and in rainy season umbrellas and rain coats have greater demand. • Rainfall data in a particular area or moisture humidity variation in coastal areas
- 24. 3. CYCLIC TREND • Cyclical variation :Cyclical variations are recurrent upward or downward movements in a time series but the period of cycle is greater than a year. • Also these variations are not regular as seasonal variation • One of the best examples for cyclical variations is „Business Cycle‟. • In this cycle there are four well defined periods or phases. *Boom * Decline * Depression * Improvement • The monthly housing demand and supply show strong seasonality within each year, as well as some strong cyclic behaviour with a period of about 6–10 years. There is no apparent trend in the data over this period.
- 25. 4. IRREGULAR TREND • Irregular variation :Irregular variations are fluctuations in time series that are short in duration, erratic in nature and follow no regularity in the occurrence pattern. • These variations are also referred to as residual variations since by definition they represent what is left out in a time series after trend ,cyclical and seasonal variations. • Eg-Study of Earthquake disaster frequency in a particular area
- 26. CONCLUSION • Thus, forecasting involves detailed analysis of the past and present events with a view to draw conclusions about future events. • And to get a clear cut idea about probable events in the future.
- 27. FORECATING IN IBM SPSS
- 28. IBM SPSS Forecasting allows you to : 1. Control every parameter when building your data model 2. Use IBM SPSS Forecasting Expert Modeler recommendations as a starting point or to check your work Features and Benefits
- 29. Procedures available in IBM SPSS Forecasting include PROCDEDURES FOR FORECASTING TSMODEL TSAPPLY SEASON SPECTRA