A Wavelet PM2.5 Prediction System Using Optimized Kernel Extreme Learning with Boruta-XGBoost Feature Selection

Heidari, Ali Asghar; Akhoondzadeh, Mehdi; Chen, Huiling

doi:10.3390/math10193566

Cited by 16 publications

(1 citation statement)

References 79 publications

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“…After outlier correction, considering the inherent nonlinearity and noise characteristics of wind speed data, applying data decomposition techniques can effectively reduce the instability of wind speed time series and eliminate redundant information. Recent studies have shown that combining data decomposition methods with advanced machine learning models can significantly improve the accuracy of wind speed predictions [27,28]. For example, Liu et al [29] utilized wavelet decomposition to reduce the volatility of time series and LSTM to extract time series features, thus constructing a hybrid model combining Wavelet Decomposition (WD) and LSTM for forecasting.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Wind Speed Prediction for Bridge Site Area Based on Wavelet Denoising OOA-Transformer

Gao,

Cao,

et al. 2024

Mathematics

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Predicting wind speed in advance at bridge sites is essential for ensuring bridge construction safety under high wind conditions. This study proposes a short-term speed prediction model based on outlier correction, Wavelet Denoising, the Osprey Optimization Algorithm (OOA), and the Transformer model. The outliers caused by data entry and measurement errors are processed by the interquartile range (IQR) method. By comparing the performance of four different wavelets, the best-performing wavelet (Bior2.2) was selected to filter out sharp noise from the data processed by the IQR method. The OOA-Transformer model was utilized to forecast short-term wind speeds based on the filtered time series data. With OOA-Transformer, the seven hyperparameters of the Transformer model were optimized by the Osprey Optimization Algorithm to achieve better performance. Given the outstanding performance of LSTM and its variants in wind speed prediction, the OOA-Transformer model was compared with six other models using the actual wind speed data from the Xuefeng Lake Bridge dataset to validate our proposed model. The experimental results show that the mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) of this paper’s method on the test set were 4.16%, 0.0152, and 0.9955, respectively, which are superior to the other six models. The prediction accuracy was found to be high enough to meet the short-term wind speed prediction needs of practical projects.

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Section: Related Workmentioning

confidence: 99%

Short-Term Wind Speed Prediction for Bridge Site Area Based on Wavelet Denoising OOA-Transformer

Gao,

Cao,

et al. 2024

Mathematics

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Designing a forecasting assistant of the Bitcoin price based on deep learning using market sentiment analysis and multiple feature extraction

Fakharchian

2023

Soft Comput

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Nowadays, the issue of fluctuations in the price of digital Bitcoin currency has a striking impact on the profit or loss of people, international relations, and trade. Accordingly, designing a model that can take into account the various significant factors for predicting the Bitcoin price with the highest accuracy is essential. Hence, the current paper presents several Bitcoin price prediction models based on Convolutional Neural Network (CNN) and Long-Short-Term Memory (LSTM) using market sentiment and multiple feature extraction. In the proposed models, several parameters, including Twitter data, news headlines, news content, Google Trends, Bitcoin-based stock, and finance, are employed based on deep learning to make a more accurate prediction. Besides, the proposed model analyzes the Valence Aware Dictionary and Sentiment Reasoner (VADER) sentiments to examine the latest news of the market and cryptocurrencies. According to the various inputs and analyses of this study, several effective feature selection methods, including mutual information regression, Linear Regression, correlation-based, and a combination of the feature selection models, are exploited to predict the price of Bitcoin. Finally, a careful comparison is made between the proposed models in terms of some performance criteria like Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Median Absolute Error (MedAE), and coefficient of determination (R 2 ). The obtained results indicate that the proposed hybrid model based on sentiments analysis and combined feature selection with MSE value of 0.001 and R 2 value of 0.98 provides better estimations with more minor errors regarding Bitcoin price. This proposed model can also be employed as an individual assistant for more informed trading decisions associated with Bitcoin.

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CEEMDAN-Based Hybrid Machine Learning Models for Time Series Forecasting Using MARS Algorithm and PSO-Optimization

Garai,

Paul,

Yeasin

et al. 2024

Neural Process Lett

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Accurate prediction of time series data is crucial for informed decision-making and economic development. However, predicting noisy time series data is a challenging task due to their irregularity and complex trends. In the past, several attempts have been made to model complex time series data using both stochastic and machine learning techniques. This study proposed a CEEMDAN-based hybrid machine learning algorithm combined with stochastic models to capture the volatility of weekly potato price in major markets of India. The smooth decomposed component is predicted using stochastic models, while the coarser components, selected using MARS, are fitted into two different machine learning algorithms. The final predictions for the original series are obtained using optimization techniques such as PSO. The performance of the proposed algorithm is measured using various metrics, and it is found that the optimization-based combination of models outperforms the individual counterparts. Overall, this study presents a promising approach to predict price series using a hybrid model combining stochastic and machine learning techniques, with feature selection and optimization techniques for improved performance.

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A Wavelet PM2.5 Prediction System Using Optimized Kernel Extreme Learning with Boruta-XGBoost Feature Selection

Cited by 16 publications

References 79 publications

Short-Term Wind Speed Prediction for Bridge Site Area Based on Wavelet Denoising OOA-Transformer

Short-Term Wind Speed Prediction for Bridge Site Area Based on Wavelet Denoising OOA-Transformer

Designing a forecasting assistant of the Bitcoin price based on deep learning using market sentiment analysis and multiple feature extraction

CEEMDAN-Based Hybrid Machine Learning Models for Time Series Forecasting Using MARS Algorithm and PSO-Optimization

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