Among various time series (TS) forecasting methods, ensemble forecast is extensively
acknowledged as a promising ensemble approach achieving great success in research and …

The key factor in selecting appropriate forecasting model is accuracy. Given the deficiencies
of single models in processing various patterns and relationships latent in data, hybrid …

Both statistical and neural methods have been proposed in the literature to predict
healthcare expenditures. However, less attention has been given to comparing predictions …

Kernel-based methods, such as support vector regression (SVR), have demonstrated
satisfactory performance in short-term load forecasting (STLF) application. However, the …

Properly comprehending and modeling the dynamics of financial data has indispensable
practical importance. The prime goal of a financial time series model is to provide reliable …

Time series forecasting is essential for various engineering applications in finance, geology,
and information technology, etc. Long Short‐Term Memory (LSTM) networks are nowadays …

Wave energy forecasting modeling is critical for promoting renewable energy storage
technology as well as for energy sustainability and global carbon neutrality goals. However …

Combining time series forecasts from several models is a fruitful alternative to using only a
single individual model. In the literature, it has been widely documented that a combined …

The absence of accurate and stable prediction of wind speed remains a major obstacle to
the rational planning, scheduling, and maintenance of wind power generation. Currently, an …

Solar activity has significant impacts on human activities and health. One most commonly
used measure of solar activity is the sunspot number. This paper compares three important …