Least Squares Method for Linear Regression:
Illustration
Applications of forecasting
Forecasting has applications in a wide range of fields where estimates of future conditions are
useful. Not everything can be forecast 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 affected by the
forecasts, the reliability of the forecasts can be significantly lower.
Climate change and increasing energy prices have led to the use of Egain Forecasting for buildings.
This attempts to reduce the energy needed to heat the building, thus reducing the emission of
greenhouse gases.
Forecasting is used in customer demand planning in everyday business for manufacturing and
distribution companies.
While the veracity of predictions for actual stock returns are disputed through reference to
the Efficient-market hypothesis, forecasting of broad economic trends is common. Such analysis is
provided by both non-profit groups as well as by for-profit private institutions.
Forecasting foreign exchange movements is typically achieved through a combination of chart
and fundamental analysis. An essential difference between chart analysis and fundamental
economic analysis is that chartists study only the price action of a market, whereas fundamentalists
attempt to look to the reasons behind the action.
Financial institutions assimilate the evidence provided by their fundamental and chartist researchers
into one note to provide a final projection on the currency in question.
Forecasting has also been used to predict the development of conflict situations by
governments.Forecasters perform research that uses empirical results to gauge the effectiveness
of certain forecasting models.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.
Similarly, experts in some studies argue that role thinking does not contribute to the accuracy of the
forecast.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. There is no single right forecasting method to use. Selection of a method should be
based on your objectives and your conditions (data etc.).
Forecasting has application in many situations:
Supply chain management- Forecasting can be used in supply chain management to ensure
that the right product is at the right place at the right time. Accurate forecasting will help retailers
reduce excess inventory and thus increase profit margin. Studies have shown that
extrapolations are the least accurate, while company earnings forecasts are the most
reliable. Accurate forecasting will also help them meet consumer demand.
Customer demand planning
Economic forecasting
Earthquake prediction
Egain forecasting
Finance against risk of default via credit ratings and credit scores
Land use forecasting
Player and team performance in sports
Political forecasting
Product forecasting
Sales forecasting
Technology forecasting.Is technological singularity possible?-technological singularity—also,
simply, the singularity—is a hypothetical point in time at which technological growth
becomes uncontrollable and irreversible, resulting in unforeseeable changes to human
civilization.
Telecommunications forecasting
Transport planning and Transportation forecasting
Weather forecasting, Flood forecasting and Meteorology
Limitations of forecasting
Forecasts are never 100% accurate. Let's face it: it's hard to predict the future. ...
It can be time-consuming and resource-intensive. Forecasting involves a lot of data
gathering, data organizing, and coordination. ...
It can also be costly.
Limitations pose barriers beyond which forecasting methods cannot reliably predict. There are many
events and values that cannot be forecast reliably. Events such as the roll of a die or the results of
the lottery cannot be forecast because they are random events and there is no significant
relationship in the data. When the factors that lead to what is being forecast are not known or well
understood such as in stock and foreign exchange markets forecasts are often inaccurate or wrong
as there is not enough data about everything that affects these markets for the forecasts to be
reliable, in addition the outcomes of the forecasts of these markets change the behavior of those
involved in the market further reducing forecast accuracy.
The concept of "self-destructing predictions" concerns the way in which some predictions can
undermine themselves by influencing social behavior. This is because "predictors are part of the
social context about which they are trying to make a prediction and may influence that context in the
process". For example, a forecast that a large percentage of a population will become HIV infected
based on existing trends may cause more people to avoid risky behavior and thus reduce the HIV
infection rate, invalidating the forecast (which might have remained correct if it had not been publicly
known). Or, a prediction that cybersecurity will become a major issue may cause organizations to
implement more security cybersecurity measures, thus limiting the issue.
