Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting
The use of wind energy plays a vital role in society owing to its economic and environmental
importance. Knowing the wind power generation within a specific time window is useful for …
importance. Knowing the wind power generation within a specific time window is useful for …
A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM
B Yan, M Aasma - Expert systems with applications, 2020 - Elsevier
Deep learning is well-known for extracting high-level abstract features from a large amount
of raw data without relying on prior knowledge, which is potentially attractive in forecasting …
of raw data without relying on prior knowledge, which is potentially attractive in forecasting …
Technical analysis strategy optimization using a machine learning approach in stock market indices
Within the area of stock market prediction, forecasting price values or movements is one of
the most challenging issue. Because of this, the use of machine learning techniques in …
the most challenging issue. Because of this, the use of machine learning techniques in …
A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network
H Niu, K Xu, W Wang - Applied Intelligence, 2020 - Springer
Abstract Changes in the composite stock price index are a barometer of social and
economic development. To improve the accuracy of stock price index prediction, this paper …
economic development. To improve the accuracy of stock price index prediction, this paper …
Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach
In this study, a novel SARIMA-SVR model is proposed to forecast statistical indicators in the
aviation industry that can be used for later capacity management and planning purpose …
aviation industry that can be used for later capacity management and planning purpose …
Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods
Background The COVID-19 pandemic conditions are still prevalent in Iran and other
countries and the monitoring system is gradually discovering new cases every day …
countries and the monitoring system is gradually discovering new cases every day …
Forecasting Nickel futures price based on the empirical wavelet transform and gradient boosting decision trees
Q Gu, Y Chang, N **ong, L Chen - Applied Soft Computing, 2021 - Elsevier
To improve the prediction accuracy of futures price, this paper proposes a hybrid approach
based on gradient boosting decision tree (GBDT), correlation analysis, and empirical …
based on gradient boosting decision tree (GBDT), correlation analysis, and empirical …
Evaluation of forecasting methods from selected stock market returns
Forecasting stock market returns is one of the most effective tools for risk management and
portfolio diversification. There are several forecasting techniques in the literature for …
portfolio diversification. There are several forecasting techniques in the literature for …
Forecasting natural gas consumption using Bagging and modified regularization techniques
This paper develops a new approach to forecast natural gas consumption via ensembles. It
combines Bootstrap Aggregation (Bagging), univariate time series forecasting methods and …
combines Bootstrap Aggregation (Bagging), univariate time series forecasting methods and …
Impact of decomposition on time series bagging forecasting performance
Time series bagging has been deemed an effective way to improve unstable modelling
procedures and subsequent forecasting accuracy. However, the literature has paid little …
procedures and subsequent forecasting accuracy. However, the literature has paid little …