Consensus forecast

Used in a number of sciences, ranging from econometrics to meteorology, consensus forecasts are predictions of the future that are created by combining several separate forecasts which have often been created using different methodologies. Also known as combining forecasts, forecast averaging or model averaging (in econometrics and statistics) and committee machines, ensemble averaging or expert aggregation (in machine learning). Applications can range from forecasting the weather to predicting the annual Gross Domestic Product of a country or the number of cars a company or an individual dealer is likely to sell in a year. While forecasts are often made for future values of a time series, they can also be for one-off events such as the outcome of a presidential election or a football match. Forecasting plays a key role in any organisation's planning process as it provides insight into uncertainty. Through simulation, one will be able to assess whether proposed strategies are likely to produce the desired objectives within predefined limits. In the field of economic forecasting, the future path of the economy is intrinsic to almost every company's business outlook, and hence there is considerable demand for accurate economic forecasts. Matching this strong demand is the large volume of readily available forecast information from governments, international agencies and various private firms. Companies such as Consensus Economics and Blue Chip Economic Indicators specialise in publishing economic forecast data, with the former covering most major regions of the world as well as providing forecasts for currencies and commodities, and the latter focusing on the US economy. However, deciphering the best forecast method is no easy task, and largely depends on the objectives of the user and the constraints they are likely to face. Rather than try to identify a single best forecasting method, an alternative approach is to combine the results from independent forecasters and take an average of the forecasts.