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Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods. Usage can differ between areas of application: for example, in hydrology the terms 'forecast' and 'forecasting' are sometimes reserved for estimates of values at certain specific future times, while the term 'prediction' is used for more general estimates, such as the number of times floods will occur over a long period.

Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be up to date in order for the forecast to be as accurate as possible. In some cases the data used to predict the variable of interest is itself forecasted.^[1]

• 1Categories of forecasting methods
• 2Forecasting accuracy
• 3Seasonality and cyclic behaviour
• 5Limitations

Categories of forecasting methods[edit]

FORECASTING FUNDAMENTALS Forecast: A prediction, projection, or estimate of some future activity, event, or occurrence. Types of Forecasts - Economic forecasts o Predict a variety of economic indicators, like money supply, inflation rates, interest rates, etc. Technological forecasts o Predict rates of technological progress and innovation. Forecasting is an important part of your business strategy – here are some techniques and tools to use. Whether it’s predicting sales, measuring market impact, or understanding if you’re going to need to grow your workforce, forecasting helps businesses assess where they are and predict where they might be going in many key areas.

Qualitative vs. quantitative methods[edit]

Qualitative forecasting techniques are subjective, based on the opinion and judgment of consumers and experts; they are appropriate when past data are not available.They are usually applied to intermediate- or long-range decisions. Examples of qualitative forecasting methods are^{[citation needed]} informed opinion and judgment, the Delphi method, market research, and historical life-cycle analogy.

• Buy a print or downloadable version. Welcome to our online textbook on forecasting. This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly.
• Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date.
• With the same techniques we may find Spring SF 2 = 0.963 Fall SF 2 = 0.906 Winter SF 2 = 0.549 Forecasting by Time Series Analysis(short-range forecast) - Without using regression analysis These models are especially helpful when there is no clear upward or downward pattern in the past data to suggest a kind of linear relationship between the.
• Major Areas of Forecasting Economic Forecasting Predicts what the general business conditions will be in the future (Eg. Inflation rates, Gross National Product, Tax, Level of employment) Technology Forecasting Predicts the probability and / or possible future developments in technology (Eg.
• These techniques are good when we want to predict existing products and technologies. They often used mathematics’ techniques for forecasting. Qualitative forecasting techniques are used in the not predictable environment and when we don’t have enough data. These techniques are usually used when managers forecast launching the new.

Quantitative forecasting models are used to forecast future data as a function of past data. They are appropriate to use when past numerical data is available and when it is reasonable to assume that some of the patterns in the data are expected to continue into the future.These methods are usually applied to short- or intermediate-range decisions. Examples of quantitative forecasting methods are^{[citation needed]} last period demand, simple and weighted N-Period moving averages, simple exponential smoothing, poisson process model based forecasting ^[2] and multiplicative seasonal indexes. Previous research shows that different methods may lead to different level of forecasting accuracy. For example, GMDH neural network was found to have better forecasting performance than the classical forecasting algorithms such as Single Exponential Smooth, Double Exponential Smooth, ARIMA and back-propagation neural network.^[3]

Average approach[edit]

In this approach, the predictions of all future values are equal to the mean of the past data. This approach can be used with any sort of data where past data is available. In time series notation:

${\displaystyle {\hat{y}}_{T+hT}=\overline{y}=(y_1+...+y_T)/T}$^[4]

where ${\displaystyle y_1,...,y_T}$ is the past data.

Although the time series notation has been used here, the average approach can also be used for cross-sectional data (when we are predicting unobserved values; values that are not included in the data set). Then, the prediction for unobserved values is the average of the observed values.

Naïve approach[edit]

Naïve forecasts are the most cost-effective forecasting model, and provide a benchmark against which more sophisticated models can be compared. This forecasting method is only suitable for time series data.^[4] Using the naïve approach, forecasts are produced that are equal to the last observed value. This method works quite well for economic and financial time series, which often have patterns that are difficult to reliably and accurately predict.^[4] If the time series is believed to have seasonality, the seasonal naïve approach may be more appropriate where the forecasts are equal to the value from last season. In time series notation:

${\displaystyle {\hat{y}}_{T+hT}=y_T}$

Drift method[edit]

A variation on the naïve method is to allow the forecasts to increase or decrease over time, where the amount of change over time (called the drift) is set to be the average change seen in the historical data. So the forecast for time ${\displaystyle T+h}$ is given by

${\displaystyle {\hat{y}}_{T+hT}=y_T+\frac{h}{T-1}\sum _{t=2}^T(y_t-y_{t-1})=y_T+h\left(\frac{y_T-y_1}{T-1}\right).}$^[4]

This is equivalent to drawing a line between the first and last observation, and extrapolating it into the future.

Seasonal naïve approach[edit]

The seasonal naïve method accounts for seasonality by setting each prediction to be equal to the last observed value of the same season. For example, the prediction value for all subsequent months of April will be equal to the previous value observed for April. The forecast for time ${\displaystyle T+h}$ is^[4]

${\displaystyle {\hat{y}}_{T+hT}=y_{T+h-km}}$

where ${\displaystyle m}$=seasonal period and ${\displaystyle k}$ is the smallest integer greater than ${\displaystyle (h-1)/m}$.

The seasonal naïve method is particularly useful for data that has a very high level of seasonality.

Time series methods[edit]

Time series methods use historical data as the basis of estimating future outcomes.

• Autoregressive moving average (ARMA) (forecasts depend on past values of the variable being forecasted and on past prediction errors)
• Autoregressive integrated moving average (ARIMA) (ARMA on the period-to-period change in the forecasted variable)

e.g. Box–Jenkins

Seasonal ARIMA or SARIMA or ARIMARCH,

• Trend estimation (predicting the variable as a linear or polynomial function of time)

Causal / econometric forecasting methods[edit]

Some forecasting methods try to identify the underlying factors that might influence the variable that is being forecast. For example, including information about climate patterns might improve the ability of a model to predict umbrella sales. Forecasting models often take account of regular seasonal variations. In addition to climate, such variations can also be due to holidays and customs: for example, one might predict that sales of college football apparel will be higher during the football season than during the off season.^[5]

Several informal methods used in causal forecasting do not rely solely on the output of mathematical algorithms, but instead use the judgment of the forecaster. Some forecasts take account of past relationships between variables: if one variable has, for example, been approximately linearly related to another for a long period of time, it may be appropriate to extrapolate such a relationship into the future, without necessarily understanding the reasons for the relationship.

Causal methods include:

• Regression analysis includes a large group of methods for predicting future values of a variable using information about other variables. These methods include both parametric (linear or non-linear) and non-parametric techniques.
• Autoregressive moving average with exogenous inputs (ARMAX)^[6]

Quantitative forecasting models are often judged against each other by comparing their in-sample or out-of-sample mean square error, although some researchers have advised against this.^[7] Different forecasting approaches have different levels of accuracy. For example, it was found in one context that GMDH has higher forecasting accuracy than traditional ARIMA ^[8]

Judgmental methods[edit]

Judgmental forecasting methods incorporate intuitive judgement, opinions and subjective probability estimates. Judgmental forecasting is used in cases where there is lack of historical data or during completely new and unique market conditions.^[9]

Judgmental methods include:

• Composite forecasts^{[citation needed]}
• Cooke's method^{[citation needed]}

Artificial intelligence methods[edit]

Often these are done today by specialized programs loosely labeled

Other methods[edit]

• Probabilistic forecasting and Ensemble forecasting

Forecasting accuracy[edit]

The forecast error (also known as a residual) is the difference between the actual value and the forecast value for the corresponding period:

${\displaystyle E_t=Y_t-F_t}$

where E is the forecast error at period t, Y is the actual value at period t, and F is the forecast for period t.

A good forecasting method will yield residuals that are uncorrelated. If there are correlations between residual values, then there is information left in the residuals which should be used in computing forecasts. This can be accomplished by computing the expected value of a residual as a function of the known past residuals, and adjusting the forecast by the amount by which this expected value differs from zero.

A good forecasting method will also have zero mean. If the residuals have a mean other than zero, then the forecasts are biased and can be improved by adjusting the forecasting technique by an additive constant that equals the mean of the unadjusted residuals.

Measures of aggregate error:

Business forecasters and practitioners sometimes use different terminology. They refer to the PMAD as the MAPE, although they compute this as a volume weighted MAPE.^[10] For more information see Calculating demand forecast accuracy.

When comparing the accuracy of different forecasting methods on a specific data set, the measures of aggregate error are compared with each other and the method that yields the lowest error is preferred.

Training and test sets[edit]

When evaluating the quality of forecasts, it is invalid to look at how well a model fits the historical data; the accuracy of forecasts can only be determined by considering how well a model performs on new data that were not used when fitting the model. When choosing models, it is common to use a portion of the available data for fitting, and use the rest of the data for testing the model, as was done in the above examples.^[11]

Cross-validation[edit]

Cross-validation is a more sophisticated version of training a test set.

For cross-sectional data, one approach to cross-validation works as follows:

1. Select observation i for the test set, and use the remaining observations in the training set. Compute the error on the test observation.
2. Repeat the above step for i = 1,2,.., N where N is the total number of observations.
3. Compute the forecast accuracy measures based on the errors obtained.

This makes efficient use of the available data, as only one observation is omitted at each step

For time series data, the training set can only include observations prior to the test set. Therefore, no future observations can be used in constructing the forecast. Suppose k observations are needed to produce a reliable forecast; then the process works as follows:

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1. Starting with i=1, select the observation k + i for the test set, and use the observations at times 1, 2, .., k+i–1 to estimate the forecasting model. Compute the error on the forecast for k+i.
2. Repeat the above step for i = 2,..,T–k where T is the total number of observations.
3. Compute the forecast accuracy over all errors.

This procedure is sometimes known as a 'rolling forecasting origin' because the 'origin' (k+i -1) at which the forecast is based rolls forward in time.^[12] Further, two-step-ahead or in general p-step-ahead forecasts can be computed by first forecasting the value immediately after the training set, then using this value with the training set values to forecast two periods ahead, etc.

Cash Flow Forecasting Techniques Pdf

See also

• Prediction intervals, similar to confidence intervals

Seasonality and cyclic behaviour[edit]

Seasonality[edit]

Seasonality is a characteristic of a time series in which the data experiences regular and predictable changes which recur every calendar year. Rslogix 5000 version 18. Any predictable change or pattern in a time series that recurs or repeats over a one-year period can be said to be seasonal. It is common in many situations – such as grocery store^[13] or even in a Medical Examiner's office^[14]—that the demand depends on the day of the week. In such situations, the forecasting procedure calculates the seasonal index of the “season” – seven seasons, one for each day – which is the ratio of the average demand of that season (which is calculated by Moving Average or Exponential Smoothing using historical data corresponding only to that season) to the average demand across all seasons. An index higher than 1 indicates that demand is higher than average; an index less than 1 indicates that the demand is less than the average.

Cyclic behaviour[edit]

The cyclic behaviour of data takes place when there are regular fluctuations in the data which usually last for an interval of at least two years, and when the length of the current cycle cannot be predetermined. Cyclic behavior is not to be confused with seasonal behavior. Seasonal fluctuations follow a consistent pattern each year so the period is always known. As an example, during the Christmas period, inventories of stores tend to increase in order to prepare for Christmas shoppers. As an example of cyclic behaviour, the population of a particular natural ecosystem will exhibit cyclic behaviour when the population decreases as its natural food source decreases, and once the population is low, the food source will recover and the population will start to increase again. Cyclic data cannot be accounted for using ordinary seasonal adjustment since it is not of fixed period.

Applications[edit]

Forecasting has applications in a wide range of fields where estimates of future conditions are useful. Not everything can be forecasted reliably, if the factors that relate to what is being forecast are known and well understood and there is a significant amount of data that can be used very reliable forecasts can often be obtained. If this is not the case or if the actual outcome is effected by the forecasts, the reliability of the forecasts can be significantly lower.^[15]

Climate change and increasing energy prices have led to the use of Egain Forecasting for buildings. This attempts to reduce the energy needed to heat the building, thus reducing the emission of greenhouse gases. Forecasting is used in Customer Demand Planning in everyday business for manufacturing and distribution companies.

While the veracity of predictions for actual stock returns are disputed through reference to the Efficient-market hypothesis, forecasting of broad economic trends is common. Such analysis is provided by both non-profit groups as well as by for-profit private institutions.^{[citation needed]}

Forecasting foreign exchange movements is typically achieved through a combination of chart and fundamental analysis. An essential difference between chart analysis and fundamental economic analysis is that chartists study only the price action of a market, whereas fundamentalists attempt to look to the reasons behind the action.^[16] Financial institutions assimilate the evidence provided by their fundamental and chartist researchers into one note to provide a final projection on the currency in question.^[17]

Forecasting has also been used to predict the development of conflict situations.^[18] Forecasters perform research that uses empirical results to gauge the effectiveness of certain forecasting models.^[19] However research has shown that there is little difference between the accuracy of the forecasts of experts knowledgeable in the conflict situation and those by individuals who knew much less.^[20]

Similarly, experts in some studies argue that role thinking^{[clarification needed]} does not contribute to the accuracy of the forecast.^[21] The discipline of demand planning, also sometimes referred to as supply chain forecasting, embraces both statistical forecasting and a consensus process. An important, albeit often ignored aspect of forecasting, is the relationship it holds with planning. Forecasting can be described as predicting what the future will look like, whereas planning predicts what the future should look like.^[22][23]There is no single right forecasting method to use. Selection of a method should be based on your objectives and your conditions (data etc.).^[24] A good place to find a method, is by visiting a selection tree. An example of a selection tree can be found here.^[25]Forecasting has application in many situations:

• Supply chain management - Forecasting can be used in supply chain management to ensure that the right product is at the right place at the right time. Accurate forecasting will help retailers reduce excess inventory and thus increase profit margin. Studies have shown that extrapolations are the least accurate, while company earnings forecasts are the most reliable.^{[clarification needed][26]} Accurate forecasting will also help them meet consumer demand.
• Finance against risk of default via credit ratings and credit scores
• Transport planning and Transportation forecasting
• Weather forecasting, Flood forecasting and Meteorology

Limitations[edit]

Limitations pose barriers beyond which forecasting methods cannot reliably predict.There are many events and values that cannot be forecast reliably. Events such as the roll of a die or the results of the lottery cannot be forecast because they are random events and there is no significant relationship in the data. When the factors that lead to what is being forecast are not known or well understood such as in stock and foreign exchange markets forecasts are often inaccurate or wrong as there is not enough data about everything that affects these markets for the forecasts to be reliable, in addition the outcomes of the forecasts of these markets change the behavior of those involved in the market further reducing forecast accuracy.^[15]

The concept of 'self-destructing predictions' concerns the way in which some predictions can undermine themselves by influencing social behavior.^[27] This is because 'predictors are part of the social context about which they are trying to make a prediction and may influence that context in the process'.^[27] For example, a forecast that a large percentage of a population will become HIV infected based on existing trends may cause more people to avoid risky behavior and thus reduce the HIV infection rate, invalidating the forecast (which might have remained correct if it had not been publicly known). Or, a prediction that cybersecurity will become a major issue may cause organizations to implement more security cybersecurity measures, thus limiting the issue.

Performance limits of fluid dynamics equations[edit]

As proposed by Edward Lorenz in 1963, long range weather forecasts, those made at a range of two weeks or more, are impossible to definitively predict the state of the atmosphere, owing to the chaotic nature of the fluid dynamics equations involved. Extremely small errors in the initial input, such as temperatures and winds, within numerical models double every five days.^[28]

See also[edit]

Business Forecasting Pdf

References[edit]

1. ^French, Jordan (2017). 'The time traveller's CAPM'. Investment Analysts Journal. 46 (2): 81–96. doi:10.1080/10293523.2016.1255469.
2. ^Mahmud, Tahmida; Hasan, Mahmudul; Chakraborty, Anirban; Roy-Chowdhury, Amit (19 August 2016). A poisson process model for activity forecasting. 2016 IEEE International Conference on Image Processing (ICIP). IEEE. doi:10.1109/ICIP.2016.7532978.
3. ^Li, Rita Yi Man; Fong, Simon; Chong, Kyle Weng Sang (2017). 'Forecasting the REITs and stock indices: Group Method of Data Handling Neural Network approach'. Pacific Rim Property Research Journal. 23 (2): 123–160. doi:10.1080/14445921.2016.1225149.
4. ^ ^abcde2.3 Some simple forecasting methods - OTexts. www.otexts.org. Retrieved 16 March 2018.
5. ^Steven Nahmias; Tava Lennon Olsen (15 January 2015). Production and Operations Analysis: Seventh Edition. Waveland Press. ISBN978-1-4786-2824-8.
6. ^Ellis, Kimberly (2008). Production Planning and Inventory Control Virginia Tech. McGraw Hill. ISBN978-0-390-87106-0.
7. ^J. Scott Armstrong and Fred Collopy (1992). 'Error Measures For Generalizing About Forecasting Methods: Empirical Comparisons'(PDF). International Journal of Forecasting. 8: 69–80. CiteSeerX10.1.1.423.508. doi:10.1016/0169-2070(92)90008-w. Archived from the original(PDF) on 2012-02-06.
8. ^16. Li, Rita Yi Man, Fong, S., Chong, W.S. (2017) Forecasting the REITs and stock indices: Group Method of Data Handling Neural Network approach, Pacific Rim Property Research Journal, 23(2), 1-38
9. ^3.1 Introduction - OTexts. www.otexts.org. Retrieved 16 March 2018.
10. ^'Response to MAPE and MPE Calculations - Mark Chockalingam - Forecasting Blog'. forecastingblog.com. 25 October 2010. Retrieved 16 March 2018.
11. ^2.5 Evaluating forecast accuracy OTexts. www.otexts.org. Retrieved 2016-05-14.
12. ^2.5 Evaluating forecast accuracy OTexts. www.otexts.org. Retrieved 2016-05-17.
13. ^Erhun, F.; Tayur, S. (2003). 'Enterprise-Wide Optimization of Total Landed Cost at a Grocery Retailer'. Operations Research. 51 (3): 343. doi:10.1287/opre.51.3.343.14953.
14. ^Omalu, B. I.; Shakir, A. M.; Lindner, J. L.; Tayur, S. R. (2007). 'Forecasting as an Operations Management Tool in a Medical Examiner's Office'. Journal of Health Management. 9: 75–84. doi:10.1177/097206340700900105.
15. ^ ^abForecasting: Principles and Practice.
16. ^Helen Allen; Mark P. Taylor (1990). 'Charts, Noise and Fundamentals in the London Foreign Exchange Market'. The Economic Journal. 100 (400): 49–59. doi:10.2307/2234183. JSTOR2234183.
17. ^Pound Sterling Live. 'Euro Forecast from Institutional Researchers', A list of collated exchange rate forecasts encompassing technical and fundamental analysis in the foreign exchange market.
18. ^T. Chadefaux (2014). 'Early warning signals for war in the news'. Journal of Peace Research, 51(1), 5-18
19. ^J. Scott Armstrong; Kesten C. Green; Andreas Graefe (2010). 'Answers to Frequently Asked Questions'(PDF). Archived from the original(PDF) on 2012-07-11. Retrieved 2012-01-23.
20. ^Kesten C. Greene; J. Scott Armstrong (2007). 'The Ombudsman: Value of Expertise for Forecasting Decisions in Conflicts'(PDF). Interfaces. 0: 1–12. Archived from the original(PDF) on 2010-06-20. Retrieved 2011-12-29.
21. ^Kesten C. Green; J. Scott Armstrong (1975). 'Role thinking: Standing in other people's shoes to forecast decisions in conflicts'(PDF). Role Thinking: Standing in Other People's Shoes to Forecast Decisions in Conflicts. 39: 111–116.
22. ^J. Scott Armstrong; Kesten C. Green; Andreas Graefe (2010). 'Answers to Frequently Asked Questions'(PDF). Archived from the original(PDF) on 2012-07-11. Retrieved 2012-01-23.
23. ^'FAQ'. Forecastingprinciples.com. 1998-02-14. Retrieved 2012-08-28.
24. ^'FAQ'. Forecastingprinciples.com. 1998-02-14. Retrieved 2012-08-28.
25. ^'Selection Tree'. Forecastingprinciples.com. 1998-02-14. Retrieved 2012-08-28.
26. ^J. Scott Armstrong (1983). 'Relative Accuracy of Judgmental and Extrapolative Methods in Forecasting Annual Earnings'(PDF). Journal of Forecasting. 2 (4): 437–447. doi:10.1002/for.3980020411.
27. ^ ^abOverland, Indra (2019-03-01). 'The geopolitics of renewable energy: Debunking four emerging myths'. Energy Research & Social Science. 49: 36–40. doi:10.1016/j.erss.2018.10.018. ISSN2214-6296.
28. ^Cox, John D. (2002). Storm Watchers. John Wiley & Sons, Inc. pp. 222–224. ISBN978-0-471-38108-2.

• Armstrong, J. Scott, ed. (2001). Principles of Forecasting: A Handbook for Researchers and Practitioners. Norwell, Massachusetts: Kluwer Academic Publishers. ISBN978-0-7923-7930-0.
• Ellis, Kimberly (2010). Production Planning and Inventory Control. McGraw-Hill. ISBN978-0-412-03471-8.
• Geisser, Seymour (June 1993). Predictive Inference: An Introduction. Chapman & Hall, CRC Press. ISBN978-0-390-87106-0.
• Gilchrist, Warren (1976). Statistical Forecasting. London: John Wiley & Sons. ISBN978-0-471-99403-9.
• Hyndman, Rob J.; Koehler, Anne B. (October–December 2006). 'Another look at measures of forecast accuracy'(PDF). International Journal of Forecasting. 22 (4): 679–688. CiteSeerX10.1.1.154.9771. doi:10.1016/j.ijforecast.2006.03.001.
• Makridakis, Spyros; Wheelwrigt, Steven; Hyndman, Rob J. (1998). Forecasting: Methods and Applications. John Wiley & Sons. ISBN978-0-471-53233-0.
• Malakooti, Behnam (February 2014). Operations and Production Systems with Multiple Objectives. John Wiley & Sons. ISBN978-0-470-03732-4.
• Kaligasidis, Angela Sasic; Taesler, Roger; Andersson, Cari; Nord, Margitta (August 2006). 'Upgraded weather forecast control of building heating systems'. In Fazio, Paul (ed.). Research in Building Physics and Building Engineering. Taylor & Francis, CRC Press. pp. 951–958. ISBN978-0-415-41675-7.
• Kress, George J.; Snyder, John (May 1994). Forecasting and Market Analysis Techniques: A Practical Approach. Quorum Books. ISBN978-0-89930-835-7.
• Rescher, Nicholas (1998). Predicting the Future: An Introduction to the Theory of Forecasting. State University of New York Press. ISBN978-0-7914-3553-3.
• Taesler, Roger (1991). 'Climate and Building Energy Management'. Energy and Buildings. 15 (1–2): 599–608. doi:10.1016/0378-7788(91)90028-2.
• Turchin, Peter (2007). 'Scientific Prediction in Historical Sociology: Ibn Khaldun meets Al Saud'. History & Mathematics: Historical Dynamics and Development of Complex Societies. Moscow: KomKniga. pp. 9–38. ISBN978-5-484-01002-8.
• US patent 6098893, Berglund, Ulf Stefan & Lundberg, Bjorn Henry, 'Comfort control system incorporating weather forecast data and a method for operating such a system', issued August 8, 2000.

External links[edit]

• Media related to Prediction at Wikimedia Commons
• Introduction to Time series Analysis (Engineering Statistics Handbook) - A practical guide to Time series analysis and forecasting

Financial Forecasting Techniques Pdf

Forecasts serve as decision support tools that allow leaders to plan for the future by performing “what-if” analyses to determine how changes in inputs affects outcomes. For example, forecasts help a business identify appropriate responses to changes in demand levels, price-cutting by the competition, economic ups and downs and more. To receive the greatest benefit from forecasts, leaders must understand the finer details of the different types of forecasting methods, recognize what a particular forecasting method type can and cannot do, and know what forecast type is best suited to a particular need.

Naive Forecasting Methods

Demand Forecasting Techniques Pdf

The naïve forecasting methods base a projection for a future period on data recorded for a past period. For example, a naïve forecast might be equal to a prior period’s actuals, or the average of the actuals for certain prior periods. Naïve forecasting makes no adjustments to past periods for seasonal variations or cyclical trends to best estimate a future period’s forecast. The user of any naïve forecasting method is not concerned with causal factors, those factors that result in a change in actuals. For this reason, the naive forecasting method is typically used to create a forecast to check the results of more sophisticated forecasting methods.

Qualitative and Quantitative Forecasting Methods

Whereas personal opinions are the basis of qualitative forecasting methods, quantitative methods rely on past numerical data to predict the future. The Delphi method, informed opinions and the historical life-cycle analogy are qualitative forecasting methods. In turn, the simple exponential smoothing, multiplicative seasonal indexes, simple and weighted moving averages are quantitative forecasting methods.

Casual Forecasting Methods

Regression analysis and autoregressive moving average with exogenous inputs are causal forecasting methods that predict a variable using underlying factors. These methods assume that a mathematical function using known current variables can be used to forecast the future value of a variable. For example, using the factor of ticket sales, you might predict the variable sale of movie-related action figures, or you might use the factor number of football games won by a university team to predict the variable sale of team-related merchandise.

Judgmental Forecasting Methods

The Delphi method, scenario building, statistical surveys and composite forecasts each are judgmental forecasting methods based on intuition and subjective estimates. The methods produce a prediction based on a collection of opinions made by managers and panels of experts or represented in a survey.

Time Series Forecasting Methods

The time series type of forecasting methods, such as exponential smoothing, moving average and trend analysis, employ historical data to estimate future outcomes. A time series is a group of data that’s recorded over a specified period, such as a company’s sales by quarter since the year 2000 or the annual production of Coca Cola since 1975. Because past patterns often repeat in the future, you can use a time series to make a long-term forecast for 5, 10 or 20 years. Long term projections are used for a number of purposes, such as allowing a company’s purchasing, manufacturing, sales and finance departments to plan for new plants, new products or new production lines.