To use a simple example, economists forecasting the impact of a vote for Brexit forecast the sudden change in the UK's political outlook and uncertainty around its future trading relationships would spook consumers and investors.
This may lead to an increase in empirical adequacy which may be welcome when it comes, for example, to forecasting the weather but not necessarily to a better understanding of the underlying mechanisms.
For example, for forecasting the value of x(t + 1) in x(1)…x t) time series, x(t – k + 1)…x t) is chosen as the inputs to multilayer network and the output will be the forecast.
For example, consider forecasting the NICU census on October 1st 2008 from September 30th 2008, the first and last date in our testing and training data, respectively.
The city, for example, forecast that capital gains would decline by 5percentt in 2002.
The J.P. Morgan team, for example, forecasts 7.6 percent for this year, and 8 percent for 2013.
Between November and March, for example, forecast venue costs rose by £78m, whereas those of site preparation, transport and infrastructure fell by £47m.
For example, PwC forecasts the market could reach 8.1 million consumers, 2.4 million small businesses and be worth £2.3bn by the end of 2018.
A higher accuracy may be required on some specific occasions, for example to forecast the possible fall-out of dangerous space debris, to assess correctly low thrust orbital transfers or complex interplanetary space missions.
Take for example, Fab.com, which is forecasting the iPad to account for a quarter of its revenue in two years' time.
For example, we first forecast the year of 2011 using the data from 2008 to 2010 as the training data.
