Hybrid Prediction Method for Wind Speed Combining Ensemble Empirical Mode Decomposition and Bayesian Ridge Regression

Yang, Yujun; Yang, Y.B.

doi:10.1109/access.2020.2984020

Cited by 31 publications

(6 citation statements)

References 54 publications

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“…Yang and Yang [22] adopted an approach, which is the combination of Bayesian ridge regression and ensemble empirical mode decomposition, to handle complex time series of wind speeds. They successfully obtained the accurate, effective, and practical significance for a wind-speed-prediction value.…”

Section: Literature Reviewmentioning

confidence: 99%

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Machine Learning Based Design of Railway Prestressed Concrete Sleepers

et al. 2022

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The state-of-the-art design methods for railway prestressed concrete sleepers are currently based on the quasi-static stresses resulting from a simplification of dynamic wheel loads, and subsequently the quasi-static responses of concrete sleepers. This method has been widely used in practices to overcome the complexity of dynamic analysis and testing. A single load factor (or called dynamic impact factor) for a partial safety-factored design (or k factors for the test criteria) is commonly used to crudely account for dynamic train–track interactions over different levels of track irregularities. The dynamic impact factors for either design or testing are usually obtained from either (i) railway infrastructure managers (i.e., in EN 13230), or (ii) prescribed standardised factors (i.e., AS 1085.14, AREMA Chapter 30, JSA—JIS E 1201). The existing design concepts for prestressed concrete sleepers using either (i) an allowable stress design or (ii) the limit state design method require many iterations for calculations and optimisations. The design process to achieve optimal products suitable for track, operational, and environmental parameters is, thus, very time-consuming. On this ground, this study investigates the potential capability of machine learning (ML) to learn from large amounts of design data sets and then to facilitate the design and capacity prediction of railway prestressed concrete sleepers. Three ML algorithms are developed, namely deep learning, Bayesian Neural Network, and random forest. Through a combination of hand-calculated design data, industry design data, and experimental investigations in compliance with EN 13230, over 3000 sets of design data have been collected. These data sets are used to assimilate a comprehensive database for machine learning. Four indicators, namely mean squared error (MSE), root-mean-square error (RMSE), mean absolute error (MAE), and R2 are used to benchmark the accuracy and precision of machine learning models. Our results reveal that the random forest algorithm offers the best performance. The values of MSE, RMSE, MAE, and R2 are 0.54, 0.74, 0.25, and 0.99, respectively. Note that the Bayesian neural network also performs very well. In contrast, the deep learning algorithm performs worse than the others. The insight demonstrates machine learning’s capability to aid in the design of railway prestressed concrete sleepers, to satisfy both serviceability and, ultimately, a limit state.

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Section: Literature Reviewmentioning

confidence: 99%

“…Bayesian network regression is a linear regression model solved by using the statistical Bayesian inference method [22]. The framework of Bayesian modelling has been praised for its ability to handle hierarchical data structures [33].…”

Section: Machine Learningmentioning

confidence: 99%

Machine Learning Based Design of Railway Prestressed Concrete Sleepers

et al. 2022

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“…There were several industries that have deployed ridge regression model as their solution. For example, in medical industry, this model was deployed in healthcare analysis system and blood-base tissue gene expression as well as in wind speed forecasting [29]- [31]. According to Manasa et al [10], the ridge regression model is a regularization model that incorporates and optimizes an additional variable (tuning parameter) to resolve the effect of multiple variables in linear regression, typically referred to as 'noise' in a statistical sense.…”

Section: • Ridge Regressionmentioning

confidence: 99%

Advanced Machine Learning Algorithms for House Price Prediction: Case Study in Kuala Lumpur

Abdul-Rahman¹,

Zulkifley²,

Ibrahim³

et al. 2021

IJACSA

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House price is affected significantly by several factors and determining a reasonable house price involves a calculative process. This paper proposes advanced machine learning (ML) approaches for house price prediction. Two recent advanced ML algorithms, namely LightGBM and XGBoost were compared with two traditional approaches: multiple regression analysis and ridge regression. This study utilizes a secondary dataset called 'Property Listing in Kuala Lumpur', gathered from Kaggle and Google Map, containing 21984 observations with 11 variables, including a target variable. The performance of the ML models was evaluated using mean absolute error (MAE), root mean square error (RMSE), and adjusted r-squared value. The findings revealed that the house price prediction model based on XGBoost showed the highest performance by generating the lowest MAE and RMSE, and the closest adjusted r-squared value to one, consistently outperformed other ML models. A new dataset which consists of 1300 samples was deployed at the model deployment stage. It was found that the percentage of the variance between the actual and predicted price was relatively small, which indicated that this model is reliable and acceptable. This study can greatly assist in predicting future house prices and the establishment of real estate policies.

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“…EEMD will separate the noise in different IMF from the original signal components [34], thus eliminating the noise-mode mixing phenomenon. In recent years, the application of EEMD has attracted the attention of many researchers and scholars [27,[35][36][37][38][39][40][41][42][43][44]. In order to solve the problem that noise in practical applications makes interference term retrieval difficult, Zhang et al [35] proposed a technique based on EEMD and EMD to achieve automatic interference term retrieval from the spectral domain low-coherence interferometry.…”

Section: Introductionmentioning

confidence: 99%

“…is method uses EEMD decomposition to make the trend signal to reflect the overall change and is not affected by high-frequency interference. To solve the difficult problem of wind speed prediction, Yang and Yang [39] proposed a hybrid BRR-EEMD short-term prediction method for wind speed based on the EMD and Bayesian ridge regression (BRR).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD

Yang

Xiao

2020

Complexity

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The stock market is a chaotic, complex, and dynamic financial market. The prediction of future stock prices is a concern and controversial research issue for researchers. More and more analysis and prediction methods are proposed by researchers. We proposed a hybrid method for the prediction of future stock prices using LSTM and ensemble EMD in this paper. We use comprehensive EMD to decompose the complex original stock price time series into several subsequences which are smoother, more regular and stable than the original time series. Then, we use the LSTM method to train and predict each subsequence. Finally, we obtained the prediction values of the original stock price time series by fused the prediction values of several subsequences. In the experiment, we selected five data to fully test the performance of the method. The comparison results with the other four prediction methods show that the predicted values show higher accuracy. The hybrid prediction method we proposed is effective and accurate in future stock price prediction. Hence, the hybrid prediction method has practical application and reference value.

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Hybrid Prediction Method for Wind Speed Combining Ensemble Empirical Mode Decomposition and Bayesian Ridge Regression

Cited by 31 publications

References 54 publications

Machine Learning Based Design of Railway Prestressed Concrete Sleepers

Machine Learning Based Design of Railway Prestressed Concrete Sleepers

Advanced Machine Learning Algorithms for House Price Prediction: Case Study in Kuala Lumpur

A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD

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