Usage can vary 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.
[1] A forecast is not to be confused with a Budget; budgets are more specific, fixed-term financial plans used for resource allocation and control, while forecasts provide estimates of future financial performance, allowing for flexibility and adaptability to changing circumstances.
[2] Climate change and increasing energy prices have led to the use of Egain Forecasting for buildings.
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.
[citation needed] Forecasting foreign exchange movements is typically achieved through a combination of historical and current data (summarized in charts) and fundamental analysis.
[6] 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.
In Philip E. Tetlock's Superforecasting: The Art and Science of Prediction, he discusses forecasting as a method of improving the ability to make decisions.
A person can become better calibrated [citation needed] — i.e. having things they give 10% credence to happening 10% of the time.
Some have claimed that forecasting is a transferable skill with benefits to other areas of discussion and decision making.
While decisions might be made based on these bets (forecasts), the main motivation is generally financial.
[13] The Groceries Code Adjudicator in the United Kingdom, which regulates supply chain management practices in the groceries retail industry, has observed that all the retailers who fall within the scope of his regulation "are striving for continuous improvement in forecasting practice and activity in relation to promotions".
[14] Qualitative forecasting techniques are subjective, based on the opinion and judgment of consumers and experts; they are appropriate when past data are not available.
Examples of quantitative forecasting methods are[citation needed] last period demand, simple and weighted N-Period moving averages, simple exponential smoothing, Poisson process model based forecasting[15] and multiplicative seasonal indexes.
This method works quite well for economic and financial time series, which often have patterns that are difficult to reliably and accurately predict.
A deterministic approach is when there is no stochastic variable involved and the forecasts depend on the selected functions and parameters.
This approach has been proposed to simulate bursts of seemingly stochastic activity, interrupted by quieter periods.
[18] Time series methods use historical data as the basis of estimating future outcomes.
For example, including information about climate patterns might improve the ability of a model to predict umbrella sales.
Causal methods include: Quantitative forecasting models are often judged against each other by comparing their in-sample or out-of-sample mean square error, although some researchers have advised against this.
For example, it was found in one context that GMDH has higher forecasting accuracy than traditional ARIMA.
[23] Judgmental forecasting methods incorporate intuitive judgement, opinions and subjective probability estimates.
Judgmental forecasting is used in cases where there is a lack of historical data or during completely new and unique market conditions.
[24] Judgmental methods include: Often these are done today by specialized programs loosely labeled Can be created with 3 points of a sequence and the "moment" or "index".
This type of extrapolation has 100% accuracy in predictions in a big percentage of known series database (OEIS).
See also 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 one-year period can be said to be seasonal.
It is common in many situations – such as grocery store[27] or even in a Medical Examiner's office[28]—that the demand depends on the day of the week.
[29] 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).
Extremely small errors in the initial input, such as temperatures and winds, within numerical models double every five days.