16. در
این
،روش
وضعیت
آینده
های داده اساس بر تنها
تاریخی
و
وض
عیت
بینی پیش حال و گذشته
نمی
شود
TREND IMPACT ANALYSIS (TIA)
TIA is a method that combines the
extrapolation of trends and the
expectations of experts about the
future, and was first introduced by
Gordon and Stover (1976)
(Hennen & Benninga, 2009).
TIA can deal with changes in trends
and allows the forecaster to specify
factors that might alter a trend, and to
assess the probabilities of their
occurrence
(Makridakis and Wheelwright, 1989).
روش
تحلیل
تأثیر
روند بر
بر تنها روش این
هایداده
نظ و نکرده تاكید كمی
ر
خبرگان
بر نیز را
هایتحلیل
كمی
افزایدمی
.
17. Was first introduced by Gordon and Stover (1976)
History of the Method
Gordon and Stover (1976)
https://doi.org/10.1016/0040-1625(76)90049-4
Using Perceptions and
Data about the Future to
Improve the Simulation of
Complex Systems
20. QUALITATIVE VS QUANTITATIVE
(Forecasting Methods)
❑ Some forecasting methods are qualitative (e.g., consensus methods in which opinions from several experts
are combined).
❑ However, most forecasting methods are quantitative; for example, trend extrapolation, simulation, cross-impact
analysis, and decision trees.
يابي برون روش کمک به ها داده تخمين
(extrapolation)
41. https://doi.org/10.1080/14620316.2009.11512589
پیش برای روند تأثیر تحلیل از استفاده
بینی
آینده در میوه مصرف
Knowledge of those aspects that motivate consumers towards more fruit consumption
is necessary in order to implement policies to stimulate fruit consumption.
Therefore, not only is a reliable prediction of fruit consumption needed,
but also knowledge about the causes of changing consumption.
42. In the TIA method, historical data and expert information are combined to produce forecasts of
future fruit consumption.
To predict future fruit consumption based on such knowledge from experts, and based on historic
consumption data, the method of Trend Impact Analysis (TIA) was applied to four countries.
•
From the historic data, a trend or baseline has been estimated.
•TIA combines this information with expert knowledge to forecast future consumption.
•A Monte Carlo simulation* was used to handle uncertainty in the TIA model thus developed.
* A Monte Carlo simulation is a model used to predict the probability of different outcomes when the
intervention of random variables is present. Monte Carlo simulations help to explain the impact of risk and
uncertainty in prediction and forecasting models. [investopedia.com]
*
سازخ شبیه
مونت
کارلو
هنی در گلف م نگایج ااگمال بین پیش براخ که است مدل
مداخله ام
شود م اسگفاده ارادف مگغیرهاخ
.
هاخ سازخ شبیه
مونت
کارلو
ری اأثیر اوضیح به
عدم و ت
کند م کم بین پیش و بین پیش هاخ مدل در اطمینان
.
43. Steps
1. Gathering of data on fruit consumption for different countries and the calculation of baselines (i.e., trends in
recent years) from these data.
2. Acquiring information on potential future trends from experts (e.g., expectations of the magnitude (impact) and
time-frame of certain factors underlying these trends).
3. Feeding baselines and the expectations of experts into the TIA model.
Consumption data
Data on fruit consumption in four countries were used
to estimate trend lines for recent years (baseline). These
data were derived from the FAO dataset (FAO, 2009)
Questionaire and interviews
Six experts from the fruit sector, and six consumer experts from outside the fruit sector, were asked
which events that influenced fruit consumption occurred up to 2007, and which events influencing fruit
consumption are likely to happen in the coming years, up to and including 2025.
For each event that an expert mentioned, he/she was asked to estimate its impact on past and
future fruit consumption (i.e., an increase or decrease) and to estimate the probability that these
events would occur at different moments in the future.
Information from the experts for the years ahead was combined, giving an equal weight to each
expert.
44. 1.Baseline data.
2.Expert data (human judgement).
3.Aggregate expert data.
4.Monte Carlo simulation.
5.Graphical presentation of the model.
63. Global GDP/capita forecasts using Trend Impact Analysis Modified by a Set of Events
داخلي ناخالص توليد
/
سرانه
Gordon, T. J. CROSS-IMPACT METHOD.
64. مآخذ و منابع
1. Hennen, W.H.G.J. & Benninga, J.. (2009). Application of Trend Impact Analysis for predicting future fruit consumption. Journal
of Horticultural Science and Biotechnology 84 (2009) 6 Isafruit Suppl. 84. 10.1080/14620316.2009.11512589.
2. Gordon, T., & Stover, J. (1976). Using perceptions and data about the future to improve the simulation of complex systems.
Technological Forecasting and Social Change, 9, 191-211.
3. Lehman-Wilzig, S., 2001. Babbling our way to a new Babel: Erasing the language barriers. The Futurist, 35(3), p.16.
4. Abbasi, Aliasghar & Saken, Hesam & Bahrami, Mohsen. (2015). Trend Impact Analysis in Futures Studies.
10.13140/2.1.3638.0485.
5. Gordon, T. (2009), ‘‘Trend impact analysis’’, in Glenn, J.C. (Ed.), Futures Research Methodology (Version 3.0), AC/UNU
Millennium Project, Washington, DC.
.6
کیانی ،علی سید ،علوی
موالن
مریم ،
( .
1397
.)
روند بر تاثیر تحلیل روش به مسکن قیمت بینی پیش
(
TIA)
،
تبریز شهر موردی مطالعه
.
انسانی روابط و جغرافیا
,
1
(
1
)
,
552
-
568
.
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غدیری
اهلل روح ،
( .
1384
.)
«
بررسی
ی مطالعه روشهای شناخت و
آینده
»
.
تهران
.
مرکز
آینده
پژوهی
و علوم
فناوری
دفاعی
مؤسس ،
ه
آموزشی
و
تحقیقاتی
صنایع
دفاعی
64