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Despite their theoretical promise their empirical accuracy in forecasting has not yet been assessed, either against each other or against any established approaches of forecast combination, model selection, or statistical benchmark algorithms.
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This traditional approach of forecasting the future has failed to predict 'credible forecasts in the past few decades' [7], has failed to satisfy the forecasting needs of end users [7] and will fail to meet the challenges of non-stationarity of climate change and other uncertain factors into the future [6, 9].
In some cases, the approach of forecasting bed requirements would be extended to other health-care units such as primary medical care, nursing home care, consultant medical care (medical care provided by a physician with specialized training), hospital care or domiciliary care [ 52].
Sfetsos [35] compares a number of approaches to forecast mean hourly wind speed.
The objective of this paper is to utilize various ANN approaches to forecast the behavior of wind velocity time series in Tehran, Iran.
Our model lifts a major limitation of traditional approaches to forecasting transnational terrorist events and can provide early warnings of emerging threats.
We approach the absorption of forecast errors by utilizing a fleet of electric vehicles whose on-premise presence is used to compose a variable energy storage.
A commonly used approach to forecast the behaviour of reaction networks is to perform computational simulations of such systems and analyse their outcome statistically.
A novel neural network approach to forecasting of financial time series based on the presentation of the series as a combination of quasiperiodic components is presented.
Among different approaches of river flow forecasting, data driven approaches have become increasingly popular in recent years due to their minimum information requirements and ability to simulate nonlinear and non-stationary characteristics of hydrological processes.
Our first aim was to evaluate the general applicability of this new approach of yield forecasting empirically at different phenological stages.
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