Forecasting is the process of making predictions of the future based on past and present data and 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, crosssectional 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.^{[1]}
Contents

Categories of forecasting methods 1

Qualitative vs. quantitative methods 1.1

Average approach 1.2

Naïve approach 1.3

Drift method 1.4

Seasonal naïve approach 1.5

Time series methods 1.6

Causal / econometric forecasting methods 1.7

Judgmental methods 1.8

Artificial intelligence methods 1.9

Other methods 1.10

Forecasting accuracy 2

Seasonality and cyclic behaviour 3

Seasonality 3.1

Cyclic behaviour 3.2

Applications 4

Limitations 5

Performance limits of fluid dynamics equations 5.1

Complexity introduced by the technological singularity 5.2

See also 6

References 7

External links 8
Categories of forecasting methods
Qualitative vs. quantitative methods
Qualitative forecasting techniques are subjective, based on the opinion and judgment of consumers, experts; they are appropriate when past data are not available. They are usually applied to intermediate or longrange decisions. Examples of qualitative forecasting methods are informed opinion and judgment, the Delphi method, market research, and historical lifecycle analogy.
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 intermediaterange decisions. Examples of quantitative forecasting methods are last period demand, simple and weighted NPeriod moving averages, simple exponential smoothing, and multiplicative seasonal indexes.
Average approach
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:
\hat{y}_{T+hT} = \bar{y} = ( y_1 + ... + y_T ) / T ^{[2]}
where y_1, ... , y_T is the past data.
Naïve approach
Naïve forecasts are the most costeffective forecasting model, and provide a benchmark against which more sophisticated models can be compared. In time series data, using naive approach would produce forecasts 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.^{[2]} If the time series is believed to have seasonality, seasonal naive approach may be more appropriate where the forecasts are equal to the value from last season. The naive method may also use a drift, which will take the last observation plus the average change from the first observation to the last observation.^{[2]} In time series notation:
\hat{y}_{T+hT} = y_T
Drift method
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 T+h is given by
\hat{y}_{T+hT} = y_T + \frac{h}{T1}\sum_{t=2}^T (y_{t}y_{t1}) = y_{T}+h\left(\frac{y_{T}y_{1}}{T1}\right). ^{[2]}
This is equivalent to drawing a line between the first and last observation, and extrapolating it into the future.
Seasonal naïve approach
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 T + h is:^{[2]}
\hat{y}_{T+hT} = y_{T+hkm} where m=seasonal period and k is the smallest integer greater than (h1) / m.
The seasonal naïve method is particularly useful for data that has a very high level of seasonality.
Time series methods
Time series methods use historical data as the basis of estimating future outcomes.

e.g. BoxJenkins
Causal / econometric forecasting methods
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.^{[3]}
Several informal methods used in causal forecasting do not employ strict 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 nonlinear) and nonparametric techniques.
Quantitative forecasting models are often judged against each other by comparing their insample or outofsample mean square error, although some researchers have advised against this.^{[5]}
Judgmental methods
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.^{[6]}
Judgmental methods include:
Artificial intelligence methods
Often these are done today by specialized programs loosely labeled
Other methods

Simulation

Prediction market

Probabilistic forecasting and Ensemble forecasting

Some socioeconomic forecasters often try to include a humanist factor. They claim that humans, through deliberate action, can have a profound influence on the future. They argue that it should be regarded a real possibility within our current socioeconomic system that its future may be influenced by, to a varying degree, individuals and small groups of individuals.^{[7]} Recent popular publications like Capital in the TwentyFirst Century are regarded as major contributors to the increasingly apparent possibility of such reality. It is argued that the influence private and public investment have on our future can never be discomposed of the individual Machiavelian human character. All methods that disregard this factor can not only never accurately predict our socioeconomic future, but can even be used as strong coercion tools. Such theories are generally regarded conspiracy theories, but the increasingly worrying socioeconomic development in the world grants some of these theories a persistent credibility.
Forecasting accuracy
The forecast error is the difference between the actual value and the forecast value for the corresponding period.
\ 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.
Measures of aggregate error:
Mean absolute error (MAE)

\ MAE = \frac{\sum_{t=1}^{N} E_t}{N}

Mean Absolute Percentage Error (MAPE)

\ MAPE = 100* \frac{\sum_{t=1}^N \frac{E_t}{Y_t}}{N}

Mean Absolute Deviation (MAD)

\ MAD = \frac{\sum_{t=1}^{N} E_t}{N}

Percent Mean Absolute Deviation (PMAD)

\ PMAD = \frac{\sum_{t=1}^{N} E_t}{\sum_{t=1}^{N} Y_t}

Mean squared error (MSE) or Mean squared prediction error (MSPE)

\ MSE = \frac{\sum_{t=1}^N {E_t^2}}{N}

Root Mean squared error (RMSE)

\ RMSE = \sqrt{\frac{\sum_{t=1}^N {E_t^2}}{N}}

Forecast skill (SS)

\ SS = 1 \frac{MSE_{forecast}}{MSE_{ref}}

Average of Errors (E)

\ \bar{E}= \frac{\sum_{i=1}^N {E_i}}{N}

Business forecasters and practitioners sometimes use different terminology in the industry. They refer to the PMAD as the MAPE, although they compute this as a volume weighted MAPE.^{[8]} For more information see Calculating demand forecast accuracy.
Limitations of Errors
The two most popular measures of accuracy that incorporate the forecast error are the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE). Thus these measures are considered to be scaledependent, that is, they are on the same scale as the original data. Consequently these cannot be used to compare models of differing scales.
Percentage errors are simply forecast errors converted into percentages and are given by P_t=100E_t/Y_t. A common accuracy measure that utilizes this is the Mean Absolute Percentage Error (MAPE). This allows for comparison between data on different scales. However, percentage errors are not quite meaningful when Y_t is close to or equal to zero, which results in extreme values or simply being undefined. ^{[9]}
See also
Seasonality and cyclic behaviour
Seasonality
Main article: Seasonality
Seasonality is a characteristic of a time series in which the data experiences regular and predictable changes which recur every calendar year. Any predictable change or pattern in a time series that recurs or repeats over a oneyear period can be said to be seasonal. It is common in many situations – such as grocery store^{[10]} or even in a Medical Examiner’s office^{[11]}—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
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 increases 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
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.^{[12]}
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 Efficientmarket hypothesis, forecasting of broad economic trends is common. Such analysis is provided by both nonprofit groups as well as by forprofit private institutions (including brokerage houses^{[13]} and consulting companies^{[14]}).
Forecasting has also been used to predict the development of conflict situations. Forecasters perform research that uses empirical results to gauge the effectiveness of certain forecasting models.^{[15]} 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.^{[16]}
Similarly, experts in some studies argue that role thinking does not contribute to the accuracy of the forecast.^{[17]} 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.^{[18]}^{[19]} There is no single right forecasting method to use. Selection of a method should be based on your objectives and your conditions (data etc.).^{[20]} A good place to find a method, is by visiting a selection tree. An example of a selection tree can be found here.^{[21]} Forecasting has application in many situations:
Limitations
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 effects 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.^{[23]}
Performance limits of fluid dynamics equations
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.^{[24]}
Complexity introduced by the technological singularity
The technological singularity is the hypothetical emergence of superintelligence through technological means.^{[25]} Since the capabilities of such intelligence would be difficult for an unaided human mind to comprehend, the technological singularity is seen as an occurrence beyond which events cannot be predicted.
Ray Kurzweil predicts the singularity will occur around 2045 while Vernor Vinge predicts it will happen some time before 2030.
See also
References

^ Scott Armstrong, Fred Collopy, Andreas Graefe and Kesten C. Green. "Answers to Frequently Asked Questions". Retrieved May 15, 2013.

^ ^{a} ^{b} ^{c} ^{d} ^{e} https://www.otexts.org/fpp/2/3

^ Nahmias, Steven (2009). Production and Operations Analysis.

^ Ellis, Kimberly (2008). Production Planning and Inventory Control Virginia Tech. McGraw Hill.

^

^ https://www.otexts.org/fpp/3/1

^ [1]http://www.iamthewitness.com/DarylBradfordSmith_Bankers.htm

^ http://www.forecastingblog.com/?p=134

^ https://www.otexts.org/fpp/2/5

^ Erhun, F.; Tayur, S. (2003). "EnterpriseWide Optimization of Total Landed Cost at a Grocery Retailer". Operations Research 51 (3): 343.

^ 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.

^ https://www.otexts.org/fpp/1/1

^ Fidelity. "2015 Stock Market Outlook", a sample outlook report by a brokerage house.

^ McKinsey Insights & Publications. "Insights & Publications".

^ J. Scott Armstrong, Kesten C. Green and Andreas Graefe (2010). "Answers to Frequently Asked Questions" (PDF).

^ Kesten C. Greene and J. Scott Armstrong (2007). "The Ombudsman: Value of Expertise for Forecasting Decisions in Conflicts" (PDF). Interfaces (INFORMS) 0: 1–12.

^ Kesten C. Green and 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.

^ "FAQ". Forecastingprinciples.com. 19980214. Retrieved 20120828.

^ Kesten C. Greene and J. Scott Armstrong. [http://www.qbox.wharton.upenn.edu/documents/mktg/research/INTFOR3581%20%20Publication% 2015.pdf "Structured analogies for forecasting"] (PDF). qbox.wharton.upenn.edu.

^ "FAQ". Forecastingprinciples.com. 19980214. Retrieved 20120828.

^ "Selection Tree". Forecastingprinciples.com. 19980214. Retrieved 20120828.

^ J. Scott Armstrong (1983). "Relative Accuracy of Judgmental and Extrapolative Methods in Forecasting Annual Earnings" (PDF). Journal of Forecasting 2: 437–447.

^ https://www.otexts.org/fpp/1/1

^ Cox, John D. (2002). Storm Watchers. John Wiley & Sons, Inc. pp. 222–224.

^ Superintelligence. Answer to the 2009 EDGE QUESTION: "WHAT WILL CHANGE EVERYTHING?": http://www.nickbostrom.com/views/superintelligence.pdf


Ellis, Kimberly (2010). Production Planning and Inventory Control. McGrawHill.


Gilchrist, Warren (1976). Statistical Forecasting. London: John Wiley & Sons.

Hyndman, R.J., Koehler, A.B (2005) "Another look at measures of forecast accuracy", Monash University note.

Makridakis, Spyros; Wheelwright, Steven;

Kress, George J.; Snyder, John (30 May 1994). Forecasting and market analysis techniques: a practical approach. Westport, Connecticut, London: Quorum Books.


Sasic Kaligasidis, A et al. (2006) Upgraded weather forecast control of building heating systems. p. 951 ff in Research in Building Physics and Building Engineering Paul Fazio (Editorial Staff), ISBN 0415416752

Taesler, R. (1990/91) Climate and Building Energy Management. Energy and Buildings, Vol. 1516, pp 599 – 608.

Turchin, P. (2007) "Scientific Prediction in Historical Sociology: Ibn Khaldun meets Al Saud". In:History & Mathematics: Historical Dynamics and Development of Complex Societies. Moscow: KomKniga. ISBN 9785484010028

United States Patent 6098893 Comfort control system incorporating weather forecast data and a method for operating such a system (Inventor Stefan Berglund)

Malakooti, B. (2013). Operations and Production Systems with Multiple Objectives. John Wiley & Sons.
External links

"Evidencebased forecasting"Forecasting Principles:

International Institute of Forecasters

Introduction to Time series Analysis (Engineering Statistics Handbook)  A practical guide to Time series analysis and forecasting

Time Series Analysis

Global Forecasting with IFs

Earthquake Electromagnetic Precursor Research

Forecasting Science and Theory of Forecasting
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