query: the delphi method of forecasting is useful  when

pos: Delphi method will decrease and the group will converge towards the "correct" answer. Finally, the process is stopped after a predefined stop criterion (e.g., number of rounds, achievement of consensus, stability of results), and the mean or median scores of the final rounds determine the results. Delphi is based on the principle that forecasts (or decisions) from a structured group of individuals are more accurate than those from unstructured groups. The technique can also be adapted for use in face-to-face meetings, and is then called mini-Delphi or Estimate-Talk-Estimate (ETE). Delphi has been widely used for business forecasting and has certain advantages over

pos: Delphi method Other forecasts of technology were dealing with vehicle-highway systems, industrial robots, intelligent internet, broadband connections, and technology in education. Later the Delphi method was applied in other places, especially those related to public policy issues, such as economic trends, health and education. It was also applied successfully and with high accuracy in business forecasting. For example, in one case reported by Basu and Schroeder (1977), the Delphi method predicted the sales of a new product during the first two years with inaccuracy of 3–4% compared with actual sales. Quantitative methods produced errors of 10–15%, and traditional unstructured forecast methods had

neg: Delphi method that the occurrence of one event may change probabilities of other events covered in the survey. Still the Delphi method can be used most successfully in forecasting single scalar indicators. Despite these shortcomings, today the Delphi method is a widely accepted forecasting tool and has been used successfully for thousands of studies in areas varying from technology forecasting to drug abuse. Delphi has characteristics similar to prediction markets as both are structured approaches that aggregate diverse opinions from groups. Yet, there are differences that may be decisive for their relative applicability for different problems. Some advantages of prediction markets derive

neg: Delphi method argument evaluation. The computerization of Argument Delphi, was developed by Sadi Evren Seker, proposes solutions to such problems. Overall the track record of the Delphi method is mixed. There have been many cases when the method produced poor results. Still, some authors attribute this to poor application of the method and not to the weaknesses of the method itself. It must also be realized that in areas such as science and technology forecasting, the degree of uncertainty is so great that exact and always correct predictions are impossible, so a high degree of error is to be expected. Another particular

neg: Wideband delphi Wideband delphi The Wideband Delphi estimation method is a consensus-based technique for estimating effort. It derives from the Delphi method which was developed in the 1950-1960s at the RAND Corporation as a forecasting tool. It has since been adapted across many industries to estimate many kinds of tasks, ranging from statistical data collection results to sales and marketing forecasts. Barry Boehm and John A. Farquhar originated the Wideband variant of the Delphi method in the 1970s. They called it "wideband" because, compared to the existing delphi method, the new method involved greater interaction and more communication among those participating. The

neg: Delphi method another structured forecasting approach, prediction markets. The name "Delphi" derives from the Oracle of Delphi, although the authors of the method were unhappy with the oracular connotation of the name, "smacking a little of the occult". The Delphi method is based on the assumption that group judgments are more valid than individual judgments. The Delphi method was developed at the beginning of the Cold War to forecast the impact of technology on warfare. In 1944, General Henry H. Arnold ordered the creation of the report for the U.S. Army Air Corps on the future technological capabilities that might be used

neg: Delphi method from the possibility to provide incentives for participation. Delphi seems to have these advantages over prediction markets: Delphi method The Delphi method ( ) is a structured communication technique or method, originally developed as a systematic, interactive forecasting method which relies on a panel of experts. The experts answer questionnaires in two or more rounds. After each round, a facilitator or change agent provides an anonymised summary of the experts' forecasts from the previous round as well as the reasons they provided for their judgments. Thus, experts are encouraged to revise their earlier answers in light of the replies of

neg: Uma Gupta on her website. Her two most cited works are "Theory and applications of the Delphi technique: A bibliography (1975–1994)" an investigation on the Delphi Technique or Delphi Method, a forecasting method that was developed on the first years of the Cold War. This work surveys the literature from 1975 to 1994 for the methodology and applications on the Delphi method. The second work is "Outsourcing the IS function: Is it necessary for your organization?" is a research on outsourcing the IS function on companies, providing help to companies trying to decide if outsourcing is a viable option or not. Uma

neg: Forecasting data are not available. They are usually applied to intermediate- or long-range decisions. Examples of qualitative forecasting methods are informed opinion and judgment, the Delphi method, market research, and historical life-cycle 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 intermediate-range decisions. Examples of quantitative forecasting methods are last period demand, simple

neg: Delphi method Delphi method The Delphi method ( ) is a structured communication technique or method, originally developed as a systematic, interactive forecasting method which relies on a panel of experts. The experts answer questionnaires in two or more rounds. After each round, a facilitator or change agent provides an anonymised summary of the experts' forecasts from the previous round as well as the reasons they provided for their judgments. Thus, experts are encouraged to revise their earlier answers in light of the replies of other members of their panel. It is believed that during this process the range of the answers

neg: Weather forecasting the conditions tomorrow. This can be a valid way of forecasting the weather when it is in a steady state, such as during the summer season in the tropics. This method of forecasting strongly depends upon the presence of a stagnant weather pattern. Therefore, when in a fluctuating weather pattern, this method of forecasting becomes inaccurate. It can be useful in both short range forecasts and long range forecasts. Measurements of barometric pressure and the pressure tendency (the change of pressure over time) have been used in forecasting since the late 19th century. The larger the change in pressure, especially

neg: Wideband delphi is the estimation session, in which the team revises the estimates as a group and achieves consensus. After the estimation session, the project manager summarizes the results and reviews them with the team, at which point they are ready to be used as the basis for planning the project. Wideband delphi The Wideband Delphi estimation method is a consensus-based technique for estimating effort. It derives from the Delphi method which was developed in the 1950-1960s at the RAND Corporation as a forecasting tool. It has since been adapted across many industries to estimate many kinds of tasks, ranging from statistical

neg: Futures techniques Futures techniques Futures techniques used in the multi-disciplinary field of futures studies by futurists in Americas and Australasia, and futurology by futurologists in EU, include a diverse range of forecasting methods, including anticipatory thinking, backcasting, simulation, and visioning. Some of the anticipatory methods include, the delphi method, causal layered analysis, environmental scanning, morphological analysis, and scenario planning. The Delphi method is a very popular technique used in Futures Studies. It was developed by Gordon and Helmer in 1953 at RAND. It can be defined as a method for structuring a group communication process, so that the process is effective in

neg: Delphi method identified. If consensus is not reached, the process continues through thesis and antithesis, to gradually work towards synthesis, and building consensus. First applications of the Delphi method were in the field of science and technology forecasting. The objective of the method was to combine expert opinions on likelihood and expected development time, of the particular technology, in a single indicator. One of the first such reports, prepared in 1964 by Gordon and Helmer, assessed the direction of long-term trends in science and technology development, covering such topics as scientific breakthroughs, population control, automation, space progress, war prevention and weapon systems.

neg: Technology forecasting virtue of the use of explicit methods is that they can be reviewed by others, and can be checked for consistency. Furthermore, the forecast can be reviewed at any subsequent time. Technology forecasting is not imagination. Commonly adopted methods of technology forecasting include the Delphi method, forecast by analogy, growth curves and extrapolation. Normative methods of technology forecasting—like the relevance trees, morphological models, and mission flow diagrams—are also commonly used. Studies of past forecasts have shown that one of the most frequent reasons why a forecast goes wrong is that the forecaster ignores related fields. A given technical approach may

neg: Delphi method by the military. Different approaches were tried, but the shortcomings of traditional forecasting methods, such as theoretical approach, quantitative models or trend extrapolation, quickly became apparent in areas where precise scientific laws have not been established yet. To combat these shortcomings, the Delphi method was developed by Project RAND during the 1950-1960s (1959) by Olaf Helmer, Norman Dalkey, and Nicholas Rescher. It has been used ever since, together with various modifications and reformulations, such as the Imen-Delphi procedure. Experts were asked to give their opinion on the probability, frequency, and intensity of possible enemy attacks. Other experts could anonymously give

neg: Weather forecasting is rarely a perfect analog for an event in the future. Some call this type of forecasting pattern recognition. It remains a useful method of observing rainfall over data voids such as oceans, as well as the forecasting of precipitation amounts and distribution in the future. A similar technique is used in medium range forecasting, which is known as teleconnections, when systems in other locations are used to help pin down the location of another system within the surrounding regime. An example of teleconnections are by using El Niño-Southern Oscillation (ENSO) related phenomena. Most end users of forecasts are members

neg: Scenario planning and consistent images thereof. Delphi, in turn, attempts to develop systematically expert opinion consensus concerning future developments and events. It is a judgmental forecasting procedure in form of an anonymous, written, multi-stage survey process, where feedback of group opinion is provided after each round. Numerous researchers have stressed that both approaches are best suited to be combined. Kinkel et al. (2006) recently reported on their experiences with both Delphi-scenarios and scenario-Delphis. The authors found that, due to their process similarity, the two methodologies can be easily combined. Generally speaking, the output of the different phases of the Delphi method can

neg: Consensus forecast additional useful information, might be captured by using the consensus approach. Another argument in favour of this method is that individual forecasts may be subject to numerous behavioural biases, but these can be minimised by combining independent forecasts together. Hence, combining is seen as helping to improve forecast accuracy by reducing the forecast errors of individual forecasts. Furthermore, averaging forecasts is likely to be more useful when the data and the forecasting techniques that the component forecasts are drawn from differ substantially. And even though it is only a simple approach (typically an unweighted mean average), this method is just

neg: Future of the Delphi method, scenario building, and simulations. Prediction is similar to forecasting but is used more generally, for instance to also include baseless claims on the future. Organized efforts to predict the future began with practices like astrology, haruspicy, and augury. These are all considered to be pseudoscience today, evolving from the human desire to know the future in advance. Modern efforts such as future studies attempt to predict technological and societal trends, while more ancient practices, such as weather forecasting, have benefited from scientific and causal modelling. Despite the development of cognitive instruments for the comprehension of future,

neg: Telecommunications forecasting statistically calculating the errors in the forecasting calculation and expressing them in terms of the root mean squared error, thereby providing an indication of the overall error in the method. A sensitivity analysis can also be useful, as it determines what will happen if some of the original data upon which the forecast was developed turned out to be wrong. Determining forecast accuracy, like forecasting itself, can never be performed with certainty and so it is advisable to ensure that input data is measured and obtained as accurately as possible, the most appropriate forecasting methods are selected, and the forecasting

neg: Futures techniques of attention to the selection of participating experts and the questionnaires have to be scrupulously prepared and tested in advance. The initial preparation and follow-up rounds of questioning of the panelists tends to be time consuming. Delphi's primary strength is its ability to explore, tranquilly and objectively, issues that require judgement. Unlike panel sessions, the iterative Delphi method allows the forecasting and assessment to be done without the effect of strong personalities or reputations influencing other panelists and also overcomes the difficulty of getting all experts together in a single time and place. This method, developed by Sohail Inayatullah, is

neg: The Wisdom of Crowds of Web-based quasi-prediction marketplace companies have sprung up to offer predictions primarily on sporting events and stock markets but also on other topics. Those companies include Piqqem, Cake Financial, Covestor, Predictify, and the Motley Fool (with its Fool CAPS product). The principle of the prediction market is also used in project management software such as Yanomo to let team members predict a project's "real" deadline and budget. The Delphi method is a systematic, interactive forecasting method which relies on a panel of independent experts. The carefully selected experts answer questionnaires in two or more rounds. After each round, a facilitator

neg: Futures techniques interact. In other words, game theory studies choice of optimal behavior when costs and benefits of each option are not fixed, but depend upon the choices of other individuals. ALL-WinWin collaborative efforts require group decision support systems (GDSS) that enable the knowledge management community of practice to assure sustainable mutually-beneficial results. Futures techniques Futures techniques used in the multi-disciplinary field of futures studies by futurists in Americas and Australasia, and futurology by futurologists in EU, include a diverse range of forecasting methods, including anticipatory thinking, backcasting, simulation, and visioning. Some of the anticipatory methods include, the delphi method, causal layered

neg: Harold A. Linstone of the professional journal "Technological Forecasting and Social Change", which he founded in 1969, and which is now in its 56th volume. In 1993 to 1994 he served as president of the International Society for the Systems Sciences. In 2003 he won the World Future Society’s Distinguished Service Award. He died on July 8, 2016 in Pasadena, California. According to Linstone and Murray Turoff (1975) the concept underlying the Delphi method is developed in defense research by the Rand Corporation sponsored by the US Air Force, which started in the early 1950s. The original goal of the research project was

neg: Affective forecasting be dedicated to enjoyable experiences, such as family outings, and in turn, provides an additional dimension to their feelings and experience of happiness. Affective forecasting provides a unique challenge to answering the question regarding the best method for increasing levels of happiness, and economists are split between offering more choices to maximize happiness, versus offering experiences that contain more "objective "or "experienced utility". Experienced utility refers to how useful an experience is in its contribution to feelings of happiness and well-being. Experienced utility can refer to both material purchases and experiential purchases. Studies show that experiential purchases, such as a

neg: UNSPSC September 29, 1998 by John S. Svendsen, the director of the Inter-agency Procurement Services Office (IAPSO) of the United Nations Development Programme (UNDP) and on November 1, 1998 by Lawrence M. Barth, a Vice President of the Dun & Bradstreet Corporation. The development of the first version was overseen by Peter R. Benson, who was also responsible for the design and development of the code management procedure as a modification of the Delphi statistical forecasting method. The process allowed for the rapid development of consensus without dominance or influence. The ECCMA, a non-profit membership association, was formed in 1999 to

neg: Nicholas Rescher focused on medieval Arabic logic, but he soon broadened his areas of inquiry in metaphysics and epistemology, moving towards the methodological pragmatism he would define. In the 1970s, he began working more extensively with American pragmatism with a focus on the writings of C. S. Peirce, who was to number among his major influences. In 1966, Rescher collaborated with Herbert A. Simon on a ground-breaking paper on the theory of causlality. He has contributed to futuristics, and with Olaf Helmer and Norman Dalkey, invented the Delphi method of forecasting. A lifelong aficionado of the philosophy of G. W. Leibniz, Rescher

neg: Plant disease forecasting must be thoroughly tested and validated after being developed. Interest has arisen lately in model validation through the quantification of the economic costs of false positives and false negatives, where disease prevention measures may be used when unnecessary or not applied when needed respectively. The costs of these two types of errors need to be weighed carefully before deciding to use a disease forecasting system. In the future, disease forecasting systems may become more useful as computing power increases and the amount of data that is available to plant pathologists to construct models increases. Good forecasting systems also may become

neg: Delphi method weakness of the Delphi method is that future developments are not always predicted correctly by consensus of experts. The issue of ignorance is important. If panelists are misinformed about a topic, the use of Delphi may only add confidence to their ignorance. One of the initial problems of the method was its inability to make complex forecasts with multiple factors. Potential future outcomes were usually considered as if they had no effect on each other. Later on, several extensions to the Delphi method were developed to address this problem, such as cross impact analysis, that takes into consideration the possibility

neg: Delphi method real-time. For instance, the TechCast Project uses a panel of 100 experts worldwide to forecast breakthroughs in all fields of science and technology. Another example is the Horizon Project, where educational futurists collaborate online using the Delphi method to come up with the technological advancements to look out for in education for the next few years. Traditionally the Delphi method has aimed at a consensus of the most probable future by iteration. Other versions, such as the Policy Delphi, is instead a decision support method aiming at structuring and discussing the diverse views of the preferred future. In Europe, more

neg: Forecasting (heating) nearly seven million square metres of floorage of residential buildings and commercial premises. Forecasting method is offered and developed by many companies and organizations. Estimated data indicate 10 - 15 kWh/m2 reduction of average annual heat energy consumption. Since forecasting method contains information about future demand and is not in conflict with other methods of increasing energy efficiency, it is always a good foreground solution. As far as practical use of forecasting method is concerned, usually remote control forecasting receivers are used to send and receive data by means of GPRS or GSM network. Then, the forecasting receivers manage the

neg: Real-time Delphi Real-time Delphi Real-time Delphi (RTD) is an advanced form of the Delphi method. The advanced method “is a consultative process that uses computer technology” to increase efficiency of the Delphi process. Gordon and Pease define the advanced approach as an innovative way to conduct Delphi studies that do not involve sequential “rounds” and consequently lead to a higher degree of efficiency with regard to the time frame needed to perform such studies. Friedewald, von Oertzen, and Cuhls underline that aspect by writing, in “a Real-Time-Delphi, the participants do not only judge twice but can change their opinion as often as

neg: Cross impact analysis Norman Dalkey used conditional probabilities, and Julius Kane developed "KSIM", a simulation technique that used interactions between time series variables rather than events. In 1974, Duperrin and Godet developed Cross Impact Systems and Matrices (or SMIC) in France for prospective forecasting studies. Advancements in simulation models continued into the 1980s. In 1980, Selwyn Enzer at the University of California incorporated cross-impact analysis into a simulation method known as Interax, The Delphi technique was combined with Cross Impact Analysis in 1984, and researchers at Texas A&M University used Cross Impact in a process called "EZ-IMPACT" that was based on Kane's algorithm

neg: Forecasting is equivalent to drawing a line between the first and last observation, and extrapolating it into the future. 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 formula_6 is where formula_8=seasonal period and formula_9 is the smallest integer greater than formula_10. The seasonal naïve method is particularly useful for data that has a very high level of seasonality. Time series methods

neg: Forecasting 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 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. Using the naïve approach, forecasts are produced that are equal to the last observed value. This method works quite

neg: Institute for the Future scenarios. This can be seen in the prospectus summary: The Institute opened in 1968, in Middletown, Connecticut. The initial group was led by Frank Davidson and included Olaf Helmer, Paul Baran, Arnold Kramish, and Theodore Gordon. The Institute’s work initially relied on the forecasting methods built upon by Helmer while at RAND. The Delphi method was used to glean information from multiple anonymous sources. It was augmented by Cross Impact Analysis, which encouraged analysts to consider multiple future scenarios. While precise and powerful, the methods that had been developed in a corporate environment were oriented to providing business and economic

neg: Delphi (IDE) such as method extraction and the possibility to create UML models from the source code or to modify the source through changes made in the model. Delphi has large communities on Usenet and the web. When Delphi was owned by Codegear, many of its employees actively participated. Each new release of Delphi attempts to be as compatible as possible with earlier versions, so that already-developed software and libraries can be retained. Incompatibility necessarily arises as new functionality is added. Although each new release of Delphi attempts to keep as much backwards compatibility as possible to allow existing code reuse, new

neg: Real-time Delphi Business Aviation and Air Cargo 4) The Indian Aerospace Industry – An Analysis of the Political, Technological and Economic Conditions 5) Transportation & Logistics 2030 – Transport infrastructure – Engine or hand brakes for global supply chains? Real-time Delphi Real-time Delphi (RTD) is an advanced form of the Delphi method. The advanced method “is a consultative process that uses computer technology” to increase efficiency of the Delphi process. Gordon and Pease define the advanced approach as an innovative way to conduct Delphi studies that do not involve sequential “rounds” and consequently lead to a higher degree of efficiency with regard

neg: Ensemble forecasting motion, a stochastic parametrisation scheme represents one possible realisation of the sub-grid. It does this through including random numbers into the equations of motion. This samples from the probability distribution assigned to uncertain processes. Stochastic parametrisations have significantly improved the skill of weather forecasting models, and are now used in operational forecasting centres worldwide. Stochastic parametrisations were first developed at the European Centre for Medium Range Weather Forecasts. When many different forecast models are used to try to generate a forecast, the approach is termed multi-model ensemble forecasting. This method of forecasting can improve forecasts when compared to a single

neg: Forecasting (heating) Forecasting (heating) Forecasting is a method of controlling building heating by calculating demand for heating energy that should be supplied to the building in each time unit. By combining physics of structures with meteorology, properties of the building, weather conditions including outdoor temperature, wind power and direction, as well as solar radiation can be taken into account. In the case of conventional heating control, only current outdoor temperature is considered. The starting point for developing the method of forecasting was the ENLOSS mathematical energy balance model developed by Prof. Roger Taesler Until 2010 inclusive, forecasting method has been introduced in

neg: Egain forecasting Egain forecasting Egain forecasting is a method of controlling building heating by calculating demand for heating energy that should be supplied to the building in each time unit. By combining physics of structures with meteorology, properties of the building, weather conditions including outdoor temperature, wind power and direction, as well as solar radiation can be taken into account. In the case of conventional heating control, only current outdoor temperature is considered. The starting point for developing the method of eGain forecasting was the ENLOSS mathematical energy balance model developed by Prof. Roger Taesler Until 2017 inclusive, forecasting method has been

neg: Delphi method errors of about 20%. (This is only one example; the overall accuracy of the technique is mixed.) The Delphi method has also been used as a tool to implement multi-stakeholder approaches for participative policy-making in developing countries. The governments of Latin America and the Caribbean have successfully used the Delphi method as an open-ended public-private sector approach to identify the most urgent challenges for their regional ICT-for-development eLAC Action Plans. As a result, governments have widely acknowledged the value of collective intelligence from civil society, academic and private sector participants of the Delphi, especially in a field of rapid change,

neg: Technology forecasting forecast is that it is wrong to some degree." Technology forecasting Technology forecasting attempts to predict the future characteristics of useful technological machines, procedures or techniques. Primarily, a technological forecast deals with the characteristics of technology, such as levels of technical performance, like speed of a military aircraft, the power in watts of a particular future engine, the accuracy or precision of a measuring instrument, the number of transistors in a chip in the year 2015, etc. The forecast does not have to state how these characteristics will be achieved. Secondly, technological forecasting usually deals with only useful machines, procedures

neg: Delphi method recent web-based experiments have used the Delphi method as a communication technique for interactive decision-making and e-democracy. The Argument Delphi, was developed by Osmo Kuusi, focuses on ongoing discussion and finding relevant arguments rather than focusing on the output. The Disaggregative Policy Delphi, developed by Petri Tapio, uses cluster analysis as a systematic tool to construct various scenarios of the future in the latest Delphi round. The respondent's view on the probable and the preferable future are dealt with as separate cases. The computerization of Argument Delphi is relatively difficult because of several problems like argument resolution, argument aggregation and

neg: Solar power forecasting Solar power forecasting Solar power forecasting involves knowledge of the Sun´s path, the atmosphere's condition, the scattering processes and the characteristics of a solar energy plant which utilizes the Sun's energy to create solar power. Solar photovoltaic systems transform solar energy into electric power. The power output depends on the incoming radiation and on the solar panel characteristics. Photovoltaic power production is increasing nowadays. Forecast information is essential for an efficient use, the management of the electricity grid and for solar energy trading. Common solar forecasting method include stochastic learning method, local and remote sensing method, and hybrid method (Chu

neg: Real-time Delphi they like when they see the aggregated results of the other participants”. So, here it becomes clear that the Real-Time Delphi approach requires real-time calculation and provision of group responses. Friedewald et al. further state that the Real-Time Delphi method has beneath its explorative and predictive elements also normative and communicative elements. These latter are investigated by Bolognini, who explores the potential of computer-based Delphi as a communication technique for electronic democracy. The basic idea of a real-time, therefore computer-based (usually web-based), Delphi approach originates in a paper published by Turoff back in 1972 about an online Delphi conference conducted

neg: Technology forecasting Technology forecasting Technology forecasting attempts to predict the future characteristics of useful technological machines, procedures or techniques. Primarily, a technological forecast deals with the characteristics of technology, such as levels of technical performance, like speed of a military aircraft, the power in watts of a particular future engine, the accuracy or precision of a measuring instrument, the number of transistors in a chip in the year 2015, etc. The forecast does not have to state how these characteristics will be achieved. Secondly, technological forecasting usually deals with only useful machines, procedures or techniques. This is to exclude from the domain

neg: Forecasting is no single right forecasting method to use. Selection of a method should be based on your objectives and your conditions (data etc.). A good place to find a method, is by visiting a selection tree. An example of a selection tree can be found here. Forecasting has application in many situations: 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

neg: Reference class forecasting the distributional information that is available". Using distributional information from previous ventures similar to the one being forecast is called taking an "outside view". Reference class forecasting is a method for taking an outside view on planned actions. Reference class forecasting for a specific project involves the following three steps: The reference class problem, also known as reference class tennis, is the discussion of which reference class to use when forecasting a given situation. Suppose someone were trying to predict how long it would take to write a psychology textbook. Reference class tennis would involve debating whether we should take

neg: Egain forecasting distribution of heat energy in the heating system of a given property. Recently, special remote control weather loggers have started to be used in combination with forecasting receivers. The weather loggers measure air temperature and humidity with high accuracy and the measurements are sent in real time to forecasting receivers to which they are connected. Such a turning point in technology means even more detailed preview of situation and better possibilities of control for users of forecasting method. Egain forecasting Egain forecasting is a method of controlling building heating by calculating demand for heating energy that should be supplied to

neg: Forecasting (heating) operation of control panels installed in buildings which adjust distribution of heat energy in the heating system of a given property. Recently, special remote control weather loggers have started to be used in combination with forecasting receivers. The weather loggers measure air temperature and humidity with high accuracy and the measurements are sent in real time to forecasting receivers to which they are connected. Such a turning point in technology indicates even higher precision of forecasting method. Forecasting (heating) Forecasting is a method of controlling building heating by calculating demand for heating energy that should be supplied to the building

neg: Egain forecasting introduced in nearly 16 million square metres of floorage of residential buildings and commercial premises. Estimated data indicate 10 - 15 kWh/"m" reduction of average annual heat energy consumption. Since forecasting method contains information about future demand and is not in conflict with other methods of increasing energy efficiency, it is always a good foreground solution. As far as practical use of forecasting method is concerned, usually remote control forecasting receivers are used to send and receive data by means of GPRS or GSM network. Then, the forecasting receivers manage the operation of control panels installed in buildings which adjust

neg: David Passig MBA programs. He is developing tools with which one can measure the skill and develop it as well. The following papers represent the taxonomy of cognitive skill that he has published: He has also developed a Future’s Research methodology named "Imen-Delphi" (ID). This methodology reflects a new paradigm in Futures' Thinking. The ID aims at structuring a procedure through which a group of experts could invent preferable futures, as opposed to the classical "Delphi" forecasting technique with which a group of experts is engaged in figuring out the most probable future. He is conducting various case studies to enhance its

neg: Prediction hard." In a non-statistical sense, the term "prediction" is often used to refer to an informed guess or opinion. A prediction of this kind might be informed by a predicting person's abductive reasoning, inductive reasoning, deductive reasoning, and experience; and may be of useful — if the predicting person is a knowledgeable person in the field. The Delphi method is a technique for eliciting such expert-judgement-based predictions in a controlled way. This type of prediction might be perceived as consistent with statistical techniques in the sense that, at minimum, the "data" being used is the predicting expert's cognitive experiences forming

neg: Oxygene (programming language) used, the compiler will add the interfaces to the class, implement them and create code to raise the events when the property changes / was changed. The modifier can be used on properties which have a setter method. The code to raise the events will then be added to this method during compile time. Program output: Some people would like to port their Win32 Delphi code to Oxygene without making major changes. This is not possible because while Oxygene looks like Delphi, there are enough changes so as to make it incompatible for a simple recompile. While the name gives

neg: Delphi running and wrestling, particularly elegant and slender under their mountain gear. Only briefly does he refer to the antiquities of Delphi, but he refers to a pelasgian wall 80 meters long, on which innumerable inscriptions are carved, decrees, conventions, manumissions". Gradually the first travelling guides appeared. The revolutionary "pocket" books invented by Karl Baedeker, accompanied by maps useful for visiting archaeological sites such as Delphi (1894) and the informed plans, the guides became practical and popular. The photographic lens revolutionized the way of depicting the landscape and the antiquities, particularly from 1893 onwards, when the systematic excavations of the French

neg: Forecasting 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: formula_16 m=seasonal period or 1 if non-seasonal 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. 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. When evaluating

neg: Delphi method feedback. This process was repeated several times until a consensus emerged. The following key characteristics of the Delphi method help the participants to focus on the issues at hand and separate Delphi from other methodologies: in this technique a panel of experts is drawn from both inside and outside the organisation. The panel consist of experts having knowledge of the area requiring decision making. Each expert is asked to make anonymous predictions. Usually all participants remain anonymous. Their identity is not revealed, even after the completion of the final report. This prevents the authority, personality, or reputation of some participants

neg: Climatology include the atmospheric boundary layer, circulation patterns, heat transfer (radiative, convective and latent), interactions between the atmosphere and the oceans and land surface (particularly vegetation, land use and topography), and the chemical and physical composition of the atmosphere. A more complicated way of making a forecast, the analog technique requires remembering a previous weather event which is expected to be mimicked by an upcoming event. What makes it a difficult technique to use is that there is rarely a perfect analog for an event in the future. Some call this type of forecasting pattern recognition, which remains a useful method

neg: Method overriding are inaccessible to . The following statements will instantiate objects of type and , and call their respective methods: In C++11, similar to Java, a method that is declared codice_1 in the super class cannot be overridden; also, a method can be declared codice_2 to make the compiler check that it overrides a method in the base class. In Delphi, method overriding is done with the directive override, but only if a method was marked with the dynamic or virtual directives. The inherited reserved word must be called when you want to call super-class behavior In Eiffel, feature redefinition is

neg: Affective forecasting to choose between one or the other. Also, when people are able to reference multiple experiences that contribute to their feelings of happiness, more opportunities for comparison will lead to a forecast of more happiness. Under these circumstances, both the quantity of choices and the quantity of experienced utility have the same effect on affective forecasting, which makes it difficult to choose a side of the debate on which method is most effective in maximizing happiness. Applying findings from affective forecasting research to happiness also raises methodological issues: should happiness measure the outcome of an experience, or the satisfaction experienced

neg: Flood forecasting locations, while "flood warning" is the task of making use of these forecasts to tell decisions on warnings of floods. Real-time flood forecasting at regional area can be done within seconds by using the technology of artificial neural network. Effective real-time flood forecasting models could be useful for early warning and disaster prevention. Flood forecasting Flood forecasting is the use of forecasted precipitation and streamflow data in rainfall-runoff and streamflow routing models to forecast flow rates and water levels for periods ranging from a few hours to days ahead, depending on the size of the watershed or river basin. Flood

neg: Solar power forecasting this sense, it is useful to classify these techniques depending on the forecasting horizon, so it is possible to distinguish between "now-casting" (forecasting 3–4 hours ahead), "short-term forecasting" (up to 7 days ahead) and "long-term forecasting" (months, years…) Solar radiation closely follows the physical and biological development of the earth. Its spatial and sequential heterogeneity powerfully influence the forcing of environmental and hydrological organisms by manipulating air temperature, soil moisture and vapor transpiration, snow cover and lots of photochemical procedures. Therefore, solar radiation drives place efficiency and plant life allotment, organism a key feature in undeveloped and forestry sciences that

neg: Product forecasting testing and volumetric forecasting. In combination with Choice Modeling it can be used to optimize offers and products. Based on the brands planned marketing mix of advertising in multiple vehicles, the ultimate brand awareness can be projected through time. Product forecasting Product forecasting is the science of predicting the degree of success a new product will enjoy in the marketplace. To do this, the forecasting model must take into account such things as product awareness, distribution, price, fulfilling unmet needs and competitive alternatives. Bass model is one type of forecasting method primarily used in new product forecasting. In general, there

neg: Delphi method such as technology policies. Use in Patent Participation Identification In the early 1980s Jackie Awerman of Jackie Awerman Associates, Inc.designed a modified Delphi method for identifying the roles of various contributors to the creation of a patent-eligible product. (Epsilon Corporation, Chemical Vapor Deposition Reactor) The results were then used by patent attorneys to determine bonus distribution percentage to the general satisfaction of all team members. From the 1970s, the use of the Delphi technique in public policy-making introduces a number of methodological innovations. In particular: Further innovations come from the use of computer-based (and later web-based) Delphi conferences. According to

neg: Mean Directional Accuracy Mean Directional Accuracy Mean Directional Accuracy (MDA), also known as Mean Direction Accuracy, is a measure of prediction accuracy of a forecasting method in statistics. It compares the forecast direction (upward or downward) to the actual realized direction. It is defined by the following formula where "A" is the actual value at time "t" and "F" is the forecast value at time "t". Variable "N" represents number of forecasting points. The function formula_2 is sign function and formula_3 is the indicator function. In simple words, MDA provides the probability that the under study forecasting method can detect the correct direction

neg: Teletraffic engineering to reduce cost in telecommunications networks. An important tool in achieving this goal is forecasting. Forecasting allows network operators to calculate the potential cost of a new network / service for a given QoS during the planning and design stage, thereby ensuring that costs are kept to a minimum. An important method used in forecasting is simulation, which is described as the most common quantitative modelling technique in use today. An important reason for this is that computing power has become far more accessible, making simulation the preferred analytical method for problems that are not easily solved mathematically. As in

neg: Plant disease forecasting plants diseases based on weather forecasts with hourly resolution of leaf wetness. Forecasting models are often based on a relationship like simple linear regression where x is used to predict y. Other relationships can be modelled using population growth curves. The growth curve that is used will depend on the nature of the epidemic. Polycyclic epidemics such as potato late blight are usually best modelled by using the logistic model, whereas monocyclic epidemics may be best modelled using the monomolecular model. Correct choice of a model is essential for a disease forecasting system to be useful. Plant disease forecasting models

neg: Olaf Helmer the United States in 1937, first working as a research assistant to Rudolf Carnap at the University of Chicago, then as a teacher of mathematics. Beginning 1944, Helmer was involved in work for the National Defense Research Council under John Williams. He would join Williams at the newly formed RAND Corporation in 1946. Helmer's interests later turned towards forecasting and prediction. Collaborating with colleagues Norman Dalkey, and Nicholas Rescher, his work led to the development of the DELPHI forecasting technique. In 1968, Helmer left RAND to co-found the Institute for the Future. In 1973 he was appointed Professor of Futuristics

neg: Product forecasting Product forecasting Product forecasting is the science of predicting the degree of success a new product will enjoy in the marketplace. To do this, the forecasting model must take into account such things as product awareness, distribution, price, fulfilling unmet needs and competitive alternatives. Bass model is one type of forecasting method primarily used in new product forecasting. In general, there will be no historical demand for new product. Then, Bass model tries to capture shape of demand of existing product and apply new product. formula_1 where, Multivariate techniques such as regression can be used to determine the values of

neg: Reference class forecasting Reference class forecasting Reference class forecasting or comparison class forecasting is a method of predicting the future by looking at similar past situations and their outcomes. Reference class forecasting predicts the outcome of a planned action based on actual outcomes in a reference class of similar actions to that being forecast. The theories behind reference class forecasting were developed by Daniel Kahneman and Amos Tversky. The theoretical work helped Kahneman win the Nobel Prize in Economics. Kahneman and Tversky found that human judgment is generally optimistic due to overconfidence and insufficient consideration of distributional information about outcomes. Therefore, people tend

neg: Technology forecasting to attempt to offset the weaknesses of one forecasting method with the strengths of another. In addition, the use of more than one forecasting method often gives the forecaster more insight into the processes at work which are responsible for the growth of the technology being forecast. A frequently used combination is that of growth curves and a trend curve for some technology. Here we see a succession of growth curves, each describing the level of functional capability achieved by a specific technical approach. An overall trend curve is also shown, fitted to those items of historical data which represent

neg: Pairs trade of time series analysis. Among those suitable for pairs trading are Ornstein-Uhlenbeck models, autoregressive moving average (ARMA) models and (vector) error correction models. Forecastability of the portfolio spread series is useful for traders because: The success of pairs trading depends heavily on the modeling and forecasting of the spread time series. Comprehensive empirical studies on pairs trading have investigated its profitability over the long-term in the US market using the distance method, co-integration, and copulas. They have found that the distance and co-integration methods result in significant alphas and similar performance, but their profits have decreased over time. Copula pairs

neg: RATF is expected to give improve decision for future system architecture and design, taking advantage of technology forecasting and innovation, thus "enabling design of tomorrow's system, today". RATF "RATF" (Robustness Analysis and Technology Forecasting) is a software development methodology acting as a plug in to the Rational Unified Process (RUP), ICONIX, Extreme Programming (XP) and Agile software development. The first part of the method was first published by in 2005 at the IASTED International conference on Software Engineering. RATF makes use of principles provided by the TRIZ innovation method and its techniques such as ARIZ and Technology forecasting, supported by Robustness

neg: Forecasting 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

neg: Solar power forecasting accuracy of the output. This is usually done with ensemble techniques that mix different outputs of different models perturbed in strategic meteorological values and finally provide a better estimate of those variables and a degree of uncertainty, like in the model proposed by Bacher et al. (2009) "Long-term" forecasting usually refers to forecasting of the annual or monthly available resource. This is useful for energy producers and to negotiate contracts with financial entities or utilities that distribute the generated energy. In general, these long-term forecasting is usually done at a lower scale than any of the two previous approaches. Hence,

neg: Cash flow forecasting debt. Cash flow is the change in cash or treasury position from one period to the next period. The direct method of cash flow forecasting schedules the company's cash receipts and disbursements (R&D). Receipts are primarily the collection of accounts receivable from recent sales, but also include sales of other assets, proceeds of financing, etc. Disbursements include payroll, payment of accounts payable from recent purchases, dividends and interest on debt. This direct R&D method is best suited to the short-term forecasting horizon of 30 days or so because this is the period for which actual, as opposed to projected, data

neg: RATF RATF "RATF" (Robustness Analysis and Technology Forecasting) is a software development methodology acting as a plug in to the Rational Unified Process (RUP), ICONIX, Extreme Programming (XP) and Agile software development. The first part of the method was first published by in 2005 at the IASTED International conference on Software Engineering. RATF makes use of principles provided by the TRIZ innovation method and its techniques such as ARIZ and Technology forecasting, supported by Robustness analysis. The novel principle provided by RATF is to elaborate on potential software evolution in a method loop consisting of the steps: Essentially the RATF method

neg: Reference class forecasting (e.g., oil and gas, chemicals, mining, energy, etc. which tend to dominate AACE's membership), benchmarking (i.e., "outside view") of project cost estimates against the historical costs of completed projects of similar types, including probabilistic information, has a long history. Reference class forecasting Reference class forecasting or comparison class forecasting is a method of predicting the future by looking at similar past situations and their outcomes. Reference class forecasting predicts the outcome of a planned action based on actual outcomes in a reference class of similar actions to that being forecast. The theories behind reference class forecasting were developed by Daniel

neg: Demand forecasting Demand forecasting Demand forecasting is a field of predictive analytics which tries to understand and predict customer demand to optimize supply decisions by corporate supply chain and business management. Demand forecasting involves quantitative methods such as the use of data, and especially historical sales data, as well as statistical techniques from test markets. Demand forecasting may be used in production planning, inventory management, and at times in assessing future capacity requirements, or in making decisions on whether to enter a new market. Forecasting demand based on expert opinion. Some of the types in this method are, a) time series projection

neg: Delphi method Method allows participants to comment on the responses of others, the progress of the panel as a whole, and to revise their own forecasts and opinions in real time. The person coordinating the Delphi method is usually known as a "facilitator" or Leader, and facilitates the responses of their "panel of experts", who are selected for a reason, usually that they hold knowledge on an opinion or view. The facilitator sends out questionnaires, surveys etc. and if the panel of experts accept, they follow instructions and present their views. Responses are collected and analyzed, then common and conflicting viewpoints are

neg: Scenario planning supply chains evolve in an energy constrained and low-carbon world; Transportation & Logistics 2030 – Transport infrastructure – Engine or hand brakes for global supply chains?; Future of Logistics – Global Scenarios 2025). Authors refer to this type as Delphi-scenario (writing), expert-based scenarios, or Delphi panel derived scenarios. Von der Gracht (2010) is a scientifically valid example of this method. Since scenario planning is “information hungry”, Delphi research can deliver valuable input for the process. There are various types of information output of Delphi that can be used as input for scenario planning. Researchers can, for example, identify relevant events

neg: Extrapolation factors. This method has been used successfully in providing forecast projections of the growth of HIV/AIDS in the UK since 1987 and variant CJD in the UK for a number of years. Another study has shown that extrapolation can produce the same quality of forecasting results as more complex forecasting strategies. Typically, the quality of a particular method of extrapolation is limited by the assumptions about the function made by the method. If the method assumes the data are smooth, then a non-smooth function will be poorly extrapolated. In terms of complex time series, some experts have discovered that extrapolation

neg: Absolute probability judgement Developed by Dalkey, the Delphi method is very similar to the Aggregated Individual Method in that experts make their initial estimates in isolation. However following this stage, the experts are then shown the outcome that all other participants have arrived at and are then able to re-consider the estimates which they initially made. The re-estimates are then aggregated using the geometric mean. This allows for some information sharing, whilst avoiding most group-led biases; however there still remains the problem of a lack of discussion. This technique takes the Delphi method and introduces limited discussion/consultation between the experts. By this means,

neg: Object Pascal and could be seen as one of the key differences that led to the Delphi style of "hiding" the heap allocation and pointer notation from the programmer. Delphi removed the need for the additional Pointer type and pointer syntax, but retained the explicit construction mechanism. Note that the object construct is still available in Delphi and Free Pascal. Note that the method implementation can also be made in a distinct location as in other Object Pascal dialects. Many features have been introduced continuously to Object Pascal with extensions to Delphi and extensions to FreePascal. In reaction to criticism, Free Pascal

neg: Climatology of observing rainfall over data voids such as oceans with knowledge of how satellite imagery relates to precipitation rates over land, as well as the forecasting of precipitation amounts and distribution in the future. A variation on this theme is used in Medium Range forecasting, which is known as teleconnections, when you use systems in other locations to help pin down the location of another system within the surrounding regime. One method of using teleconnections are by using climate indices such as ENSO-related phenomena. Climatology Climatology (from Greek , "klima", "place, zone"; and , "-logia") or climate science is the

neg: Barry Boehm program, in Person-Months (PM), to "Thousand Source Lines of Code" (KSLOC). formula_1 Where A is a calibration constant based on project data and B is an exponent for the software diseconomy of scale. Boehm also created the spiral model of software development, in which the phases of development are repeatedly revisited. This iterative software development process influenced MBASE and extreme programming. Boehm refined the Delphi method of estimation to include more group iteration, making it more suitable for certain classes of problems, such as software development. This variant is called the Wideband Delphi method. The Incremental Commitment Model (ICM) is

neg: Norman N. Holland 1995, Holland published a mystery novel based on the Delphi seminars entitled "Death in a Delphi Seminar: A Postmodern Mystery". The story takes place in an English department, and the reader is led through the text using reader-response theory to understand the characters and the crime. "Know Thyself: Delphi Seminars" (2009) by Holland and Schwartz provides an overview of the Delphi Seminar teaching style and lays out the seminar’s findings. "The I" (1985) extends the holistic method employed by Holland in his previously published case studies of readers. In the text, Holland proposes general processes of perception and symbolization that

neg: Ensemble forecasting Ensemble forecasting Ensemble forecasting is a method used in numerical weather prediction. Instead of making a single forecast of the most likely weather, a set (or ensemble) of forecasts is produced. This set of forecasts aims to give an indication of the range of possible future states of the atmosphere. Ensemble forecasting is a form of Monte Carlo analysis. The multiple simulations are conducted to account for the two usual sources of uncertainty in forecast models: (1) the errors introduced by the use of imperfect initial conditions, amplified by the chaotic nature of the evolution equations of the atmosphere, which

neg: Ensemble forecasting week high-impact weather forecasts for the benefit of humanity. Centralized archives of ensemble model forecast data, from many international centers, are used to enable extensive data sharing and research. Ensemble forecasting Ensemble forecasting is a method used in numerical weather prediction. Instead of making a single forecast of the most likely weather, a set (or ensemble) of forecasts is produced. This set of forecasts aims to give an indication of the range of possible future states of the atmosphere. Ensemble forecasting is a form of Monte Carlo analysis. The multiple simulations are conducted to account for the two usual sources

neg: Wind power forecasting farms are not situated on these nodes, it is then needed to extrapolate these forecasts at the desired location and at turbine hub height. Physical-based forecasting methods consist of several sub-models which altogether deliver the translation from the wind forecast at a certain grid point and model level, to power forecast at the site considered. Every sub-model contains the mathematical description of the physical processes relevant to the translation. Knowledge of all relevant processes is therefore crucial when developing a purely physical prediction method (such as the early versions of the Danish Prediktor). The core idea of physical approaches is

neg: Wind power forecasting accuracy of these predictions. In practice today, uncertainty is expressed in the form of probabilistic forecasts or with risk indices provided along with the traditional point predictions. It has been shown that some decisions related to wind power management and trading are more optimal when accounting for prediction uncertainty. For the example of the trading application, studies have shown that reliable estimation of prediction uncertainty allows wind power producer to significantly increase their income in comparison to the sole use of an advanced point forecasting method. Other studies of this type deal with optimal dynamic quantification of reserve requirements, optimal

neg: Wind power forecasting when evaluating more advanced approaches. The most popular of these reference methods is certainly "persistence". This naive predictor — commonly referred to as ‘what you see is what you get’ — states that the future wind generation will be the same as the last measured value. Despite its apparent simplicity, this naive method might be hard to beat for look-ahead times up to 4–6 hours ahead Advanced approaches for short-term wind power forecasting necessitate predictions of meteorological variables as input. Then, they differ in the way predictions of meteorological variables are converted to predictions of wind power production, through the

neg: The Ultimate Resource amount of resources, extrapolates the rate of use from current use and subtracts one from the other. Simon argues that these simple analyses are often wrong. While focusing only on proven resources is helpful in a business context, it is not appropriate for economy-wide forecasting. There exist undiscovered sources, sources not yet economically feasible to extract, sources not yet technologically feasible to extract, and ignored resources that could prove useful but are not yet worth trying to discover. To counter the problems of engineering forecasting, Simon proposes economic forecasting, which proceeds in three steps in order to capture, in part,

neg: Earthquake forecasting to regional earthquake forecasts, such seismic hazard calculations can take factors such as local geological conditions into account. Anticipated ground motion can then be used to guide building design criteria. Methods for earthquake forecasting generally look for trends or patterns that lead to an earthquake. As these trends may be complex and involve many variables, advanced statistical techniques are often needed to understand them, therefore these are sometimes called statistical methods. These approaches tend to have relatively long time periods, making them useful for earthquake forecasting. Even the stiffest of rock is not perfectly rigid. Given a large force (such

neg: Locating the center of mass Locating the center of mass There are several methods for locating the center of mass of a body. This method is useful when one wishes to find the centroid of a complex planar shape with unknown dimensions. It relies on finding the center of mass of a thin body of homogeneous density having the same shape as the complex planar shape. This is a method of determining the center of mass of an L-shaped object. This method is useful when one wishes to find the location of the centroid or center of mass of an object that is easily divided

neg: Delphi method Turoff and Hiltz, in computer-based Delphis: According to Bolognini, web-based Delphis offer two further possibilities, relevant in the context of interactive policy-making and e-democracy. These are: One successful example of a (partially) web-based policy Delphi is the five-round Delphi exercise (with 1,454 contributions) for the creation of the eLAC Action Plans in Latin America. It is believed to be the most extensive online participatory policy-making foresight exercise in the history of intergovernmental processes in the developing world at this time. In addition to the specific policy guidance provided, the authors list the following lessons learned include "(1) the potential of

neg: Delphi (IDE) interoperate with code developed with other software development tools, Delphi supports independent interfaces of Component Object Model (COM) with reference counted class implementations, and support for many third-party components. Interface implementations can be delegated to fields or properties of classes. Message handlers are implemented by tagging a method of a class with the integer constant of the message to handle. Database connectivity is extensively supported through the many VCL database-aware and database access components. Later versions have included upgraded and enhanced runtime library routines provided by the community group FastCode, established in 2003. Delphi is a strongly typed high-level programming

neg: Mean absolute scaled error When comparing forecasting methods, the method with the lowest MASE is the preferred method. Mean absolute scaled error In statistics, the mean absolute scaled error (MASE) is a measure of the accuracy of forecasts . It was proposed in 2005 by statistician Rob J. Hyndman and Professor of Decision Sciences Anne B. Koehler, who described it as a "generally applicable measurement of forecast accuracy without the problems seen in the other measurements." The mean absolute scaled error has favorable properties when compared to other methods for calculating forecast errors, such as root-mean-square-deviation, and is therefore recommended for determining comparative accuracy