Forecasting

Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be estimation for 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 judgemental 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. The process of climate change and increasing energy prices has led to the usage of Egain Forecasting of buildings. The method uses Forecasting to reduce the energy needed to heat the building, thus reducing the emission of greenhouse gases. Forecasting is used in the practice of Customer Demand Planning in every day business forecasting for manufacturing companies. Forecasting has also been used to predict the development of conflict situations. Research has shown that there is little difference between the accuracy of forecasts performed by experts knowledgeable of the conflict situation of interest and that performed by individuals who knew much less.[1] 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.[2][3] There is no single right forecasting method to use. Selection of a method should be based on your objectives and your conditions (data etc.).[4] A good place to find a method, is by visiting a selection tree. An example of a selection tree can be found here.[5]

Contents

Categories of forecasting methods

Qualitative vs. Quantitative Methods

Qualitative forecasting techniques are subjective, based on the opinion and judgment of consumers, experts; appropriate when past data is not available. It is usually applied to intermediate-long range decisions.

Example of qualitative forecasting methods:

Quantitative forecasting models are used to estimate future demands as a function of past data; appropriate when past data is available. It is usually applied to short-intermediate range decisions.

Example of Quantitative forecasting methods:

Naïve Approach

Naïve forecasts are the most cost-effective and efficient objective forecasting model, and provide a benchmark against which more sophisticated models can be compared. For stable time series data, this approach says that the forecast for any period equals the previous period's actual value.

Time series methods

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

e.g. Box-Jenkins

Causal / econometric forecasting methods

Some forecasting methods use the assumption that it is possible to identify the underlying factors that might influence the variable that is being forecast. For example, including information about weather conditions might improve the ability of a model to predict umbrella sales. This is a model of seasonality which shows a regular pattern of up and down fluctuations. In addition to weather, seasonality can also be due to holidays and customs such as predicting that sales in college football apparel will be higher during football season as opposed to the off season. [6]

Casual forecasting methods are also subject to the discretion of the forecaster. There are several informal methods which do not have strict algorithms, but rather modest and unstructured guidance. One can forecast based on, for example, linear relationships. If one variable is linearly related to the other for a long enough period of time, it may be beneficial to predict such a relationship in the future. This is quite different from the aforementioned model of seasonality whose graph would more closely resemble a sine or cosine wave. The most important factor when performing this operation is using concrete and substantiated data. Forecasting off of another forecast produces inconclusive and possibly erroneous results.

Such methods include:

[7]

Judgmental methods

Judgmental forecasting methods incorporate intuitive judgements, opinions and subjective probability estimates.

Artificial intelligence methods

Other methods

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 = \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) \ 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}

Please note that business forecasters and practitioners sometimes use different terminology in the industry. They refer to the PMAD as the MAPE, although they compute this volume weighted MAPE. For more information see Calculating Demand Forecast Accuracy

Reference class forecasting was developed to increase forecasting accuracy.[8]

See also

Applications of forecasting

Forecasting has application in many situations:

See also

References

  1. ^ Kesten C. Greene and J. Scott Armstrong (2007). "The Ombudsman: Value of Expertise for Forecasting Decisions in Conflicts". INFORMS. pp. 1–12. http://marketing.wharton.upenn.edu/documents/research/Value%20of%20expertise.pdf. 
  2. ^ http://www.forecastingprinciples.com/index.php?option=com_content&task=view&id=3&Itemid=3
  3. ^ Kesten C. Greene and J. Scott Armstrong (2007). [http://qbox.wharton.upenn.edu/documents/mktg/research/INTFOR3581%20-%20Publication% 2015.pdf "Structured analogies for forecasting"]. pp. 365–376. http://qbox.wharton.upenn.edu/documents/mktg/research/INTFOR3581%20-%20Publication% 2015.pdf. 
  4. ^ http://www.forecastingprinciples.com/index.php?option=com_content&task=view&id=3&Itemid=3#D._Choosing_the_best_method
  5. ^ http://www.forecastingprinciples.com/index.php?option=com_content&task=view&id=17&Itemid=17
  6. ^ Nahmias, Steven (2009). Production and Operations Analysis. 
  7. ^ Ellis, Kimberly (2008). Production Planning and Inventory Control Virginia Tech. McGraw Hill. ISBN 978-0390871060. 
  8. ^ Flyvbjerg, B., 2008, "Curbing Optimism Bias and Strategic Misrepresentation in Planning: Reference Class Forecasting in Practice." European Planning Studies, vol. 16, no. 1, January, pp. 3-21.

External links