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A novel hybrid algorithm based on Empirical Fourier decomposition and deep learning for wind speed forecasting
As the demand for renewable energy is increasing, wind speed forecasting (WSF) becomes
increasingly popular and essential for wind power generation. Several methods have been …
increasingly popular and essential for wind power generation. Several methods have been …
An efficient forecasting method for time series based on visibility graph and multi-subgraph similarity
Y Hu, F **ao - Chaos, Solitons & Fractals, 2022 - Elsevier
Recently network-based method for forecasting time series has become a hot research
topic. Although some methods have been recognized for their prediction performance, how …
topic. Although some methods have been recognized for their prediction performance, how …
Etemadi regression in chemometrics: Reliability-based procedures for modeling and forecasting
The creation of predictive models with a high degree of generalizability in chemical analysis
and process optimization is of paramount importance. Nonetheless, formulating a prediction …
and process optimization is of paramount importance. Nonetheless, formulating a prediction …
A novel framework for direct multistep prediction in complex systems
Multistep prediction is an open challenge in many real-world systems for a long time.
Despite the advantages of previous approaches, eg, step-by-step iteration, they have some …
Despite the advantages of previous approaches, eg, step-by-step iteration, they have some …
Discrete learning-based intelligent methodology for heart disease diagnosis
Classification is one of the most frequently used data mining approaches which has been
broadly applied in different fields of sciences, such as engineering, finance, energy …
broadly applied in different fields of sciences, such as engineering, finance, energy …