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RBF neural network in their models are trained to generate both time series forecasts and certainty factors.
Combining time series forecasts from several models is a fruitful alternative to using only a single individual model.
The Schaake Shuffle is used to connect forecast ensemble members of individual months to form ensemble monthly time series forecasts.
It is shown that the least squares approach to reconciling hierarchical time series forecasts can be extended to much more general collections of time series with aggregation constraints.
Perez et al. [15] forecast wind speed using a blended ensemble, which consists of the Weather Research and Forecasting Single Column Model and time series forecasts that are calibrated with Bayesian model averaging.
On the other side, the multi-step-ahead (MS) technique was indicated to improve the issue of forecasting accuracy fades rapidly, and it has a wide application in economic time series forecasts [16].
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Hybrid systems that combine Artificial Neural Networks with other forecasters have been widely employed for time series forecasting.
There are various models to time series forecasting.
Here we choose some typical time series forecasting methods.
However, no seasonal time series forecast has been tested.
Fuzzy time series forecasting is an emergent research topic.
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