Forecasting is the process of making predictions based on past and present data. These forecasts can later be compared with actual outcomes. For example, a company might estimate their revenue in the next year, then compare it against the actual results creating a variance actual analysis. Prediction is a similar but more general term. Forecasting might refer to specific formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods or the process of prediction and assessment of its accuracy. 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.Multiple sources:
Risk and uncertainty are central to forecasting and prediction; it is generally considered a good practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be up to date in order for the forecast to be as accurate as possible. In some cases the data used to predict the variable of interest is itself forecast. 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. Both tools are valuable in financial planning and decision-making, but they serve different functions.
Applications Forecasting has applications in a wide range of fields where estimates of future conditions are useful. Depending on the field, accuracy varies significantly. 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, it is likely the final value will be close to the forecast. 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 historical and current data (summarized in charts) 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.Pound Sterling Live. "Euro Forecast from Institutional Researchers", A list of collated exchange rate forecasts encompassing technical and fundamental analysis in the foreign exchange market.
Forecasting has also been used to predict the development of conflict situations.T. Chadefaux (2014). "Early warning signals for war in the news". Journal of Peace Research, 51(1), 5-18 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 — standing in other people's shoes to forecast their decisions — does not contribute to the accuracy of the forecast.
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.). A good way 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: Demand forecasting, supply chain management and customer demand planning — 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. Accurate forecasting will also help them meet consumer demand. The discipline of demand planning, also sometimes referred to as supply chain forecasting, embraces both statistical forecasting and a consensus process. Studies have shown that extrapolations are the least accurate, while company earnings forecasts are the most reliable. Economic forecasting Earthquake prediction Egain forecasting Energy forecasting for renewable power integration 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 Telecommunications forecasting Transport planning and forecasting Weather forecasting, flood forecasting and meteorology
Forecasting as training, betting and futarchy In several cases, the forecast is either more or less than a prediction of the future.
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 — i.e. having things they give 10% credence to happening 10% of the time. Or they can forecast things more confidently — coming to the same conclusion but earlier. Some have claimed that forecasting is a transferable skill with benefits to other areas of discussion and decision making.
Betting on sports or politics is another form of forecasting. Rather than being used as advice, bettors are paid based on if they predicted correctly. While decisions might be made based on these bets (forecasts), the main motivation is generally financial.
Finally, futarchy is a form of government where forecasts of the impact of government action are used to decide which actions are taken. Rather than advice, in futarchy's strongest form, the action with the best forecasted result is automatically taken.
Forecast improvements Forecast improvement projects have been operated in a number of sectors: the National Hurricane Center's Hurricane Forecast Improvement Project (HFIP) and the Wind Forecast Improvement Project sponsored by the US Department of Energy are examples.Department of Energy, The Wind Forecast Improvement Project (WFIP): A Public–Private Partnership Addressing Wind Energy Forecast Needs, published 30 October 2015, accessed 9 December 2022 In relation to supply chain management, the Du Pont model has been used to show that an increase in forecast accuracy can generate increases in sales and reductions in inventory, operating expenses and commitment of working capital.Logility, Inc. (2016), Beyond Basic Forecasting, accessed 9 December 2022 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".Groceries Code Adjudicator, Best practice statement: Forecasting and promotions, published on 25 June 2018, accessed on 6 November 2024
Categories of forecasting methods
Qualitative vs. quantitative methods
Qualitative forecasting techniques are subjective, based on the opinion and judgment of consumers and experts; they are appropriate when past 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 and weighted N-Period moving averages, simple exponential smoothing, Poisson process model based forecasting and multiplicative seasonal indexes. Previous research shows that different methods may lead to different level of forecasting accuracy. For example, GMDH neural network was found to have better forecasting performance than the classical forecasting algorithms such as Single Exponential Smooth, Double Exponential Smooth, ARIMA and back-propagation neural network.
Average approach
In this approach, the predictions of all future values are equal to the mean of the past data. This approach can be used with any sort of data where past data is available. In time series notation:
where 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 approach
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 well for economic and financial time series, which often have patterns that are difficult to reliably and accurately predict. If the time series is believed to have seasonality, the seasonal naïve approach may be more appropriate where the forecasts are equal to the value from last season. In time series notation:
Drift method A variation on the naïve method is to allow the forecasts to increase or decrease over time, where the amount of change over time (called the drift) is set to be the average change seen in the historical data. So the forecast for time is given by
This is equivalent to drawing a line between the first and last observation, and extrapolating it into the future.
Seasonal naïve approach
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 is
where is the seasonal period (for example, for monthly data with yearly seasonality, ) and is the integer part of .
The seasonal naïve method is particularly useful for data that has a very high level of seasonality.
Deterministic approach A deterministic approach is when there is no stochastic variable involved and the forecasts depend on the selected functions and parameters. For example, given the function
The short term behaviour and the is the medium-long-term trend are
where are some parameters.