[HTML][HTML] Decomposition-based wind power forecasting models and their boundary issue: An in-depth review and comprehensive discussion on potential solutions
Recently, numerous forecasting models have been reported in the wind power forecasting
field, aiming for reliable integration of renewable energy into the electric grid. Decomposition …
field, aiming for reliable integration of renewable energy into the electric grid. Decomposition …
Forecast evaluation for data scientists: common pitfalls and best practices
Recent trends in the Machine Learning (ML) and in particular Deep Learning (DL) domains
have demonstrated that with the availability of massive amounts of time series, ML and DL …
have demonstrated that with the availability of massive amounts of time series, ML and DL …
MultiRocket: multiple pooling operators and transformations for fast and effective time series classification
We propose MultiRocket, a fast time series classification (TSC) algorithm that achieves state-
of-the-art accuracy with a tiny fraction of the time and without the complex ensembling …
of-the-art accuracy with a tiny fraction of the time and without the complex ensembling …
Forecasting the future: A comprehensive review of time series prediction techniques
Time series forecasting is a critical aspect of data analysis, with applications ranging from
finance and economics to weather prediction and industrial processes. This review paper …
finance and economics to weather prediction and industrial processes. This review paper …
A real-time adaptive model for bearing fault classification and remaining useful life estimation using deep neural network
Rolling element bearings are essential components of a wide variety of industrial machinery
and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) …
and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) …
The impact of fossil fuels, renewable energy, and nuclear energy on South Korea's environment based on the STIRPAT model: ARDL, FMOLS, and CCR Approaches
This study intends to shed light on the environmental impacts of energy decisions in South
Korea by analyzing the correlation between energy consumption patterns and …
Korea by analyzing the correlation between energy consumption patterns and …
A new bearing fault diagnosis approach combining sensitive statistical features with improved multiscale permutation entropy method
Obtaining the sensitive feature vectors from the vibration signal is crucial to indicate the
bearing's actual condition. Most often, weak feature vectors are the consequence of heavy …
bearing's actual condition. Most often, weak feature vectors are the consequence of heavy …
Mixture of activation functions with extended min-max normalization for forex market prediction
An accurate exchange rate forecasting and its decision-making to buy or sell are critical
issues in the Forex market. Short-term currency rate forecasting is a challenging task due to …
issues in the Forex market. Short-term currency rate forecasting is a challenging task due to …
What is the best RNN-cell structure to forecast each time series behavior?
It is unquestionable that time series forecasting is of paramount importance in many fields.
The most used machine learning models to address time series forecasting tasks are …
The most used machine learning models to address time series forecasting tasks are …
Oil price changes and stock returns: Fresh evidence from oil exporting and oil importing countries
The study examines the vital connection between stock returns and oil price changes for oil
exporting/importing countries separately. We present evidence employing granger causality …
exporting/importing countries separately. We present evidence employing granger causality …