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This study aims to propose a forecasting scheme that enables to make forecasts up to 48 h lead time.
Taking into account the high volume of data transferred in a wireless network and the requirement of real time, we propose a forecasting methodology based on data mining.
In this paper, we propose a forecasting method specifically designed for multi-dimensional long-term trends prediction, with a double application of the Kohonen algorithm.
Accordingly, we propose a forecasting strategy that involves switching between a model of interest and the benchmark model, based on observations of their recent past performance.
In order to design efficient survivable transport network, we propose a forecasting based p-cycle (FDP) to adapt to the rapid development of information communication technology network and the communication infrastructure of further smart grid.
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Lv and Yue (2011), the authors propose a forecast model based on wavelet ARIMA-GARCH model.
This study proposes a forecast-skill-based model of forecast evolution that is able to simulate dynamically updated streamflow forecasts.
(2015) propose a hybrid forecasting methodology that combines an ensemble empirical mode decomposition algorithm with a SVR model to forecast the US real house price index.
We propose a statistical forecasting model to estimate the TE and ATE trajectories.
Apart from the literature, this work extends previous methods to propose a Switching forecast model to increase forecast effectiveness.
Attempts have also been made to propose a real forecast experiment, which, however, often cannot be able to adapt to the non-stationary changes in runoff due to the lack of adaptive ability.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com