Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling

Bitcoin has recently received a lot of attention from the media and the public due to its recent price surge and crash. Correspondingly, many researchers have investigated various factors that affect the Bitcoin price and the patterns behind its fluctuations, in particular, using various machine learning methods. In this paper, we study and compare various state-of-the-art deep learning methods such as a deep neural network (DNN), a long short-term memory (LSTM) model, a convolutional neural network, a deep residual network, and their combinations for Bitcoin price prediction. Experimental results showed that although LSTM-based prediction models slightly outperformed the other prediction models for Bitcoin price prediction (regression), DNN-based models performed the best for price ups and downs prediction (classification). In addition, a simple profitability analysis showed that classification models were more effective than regression models for algorithmic trading. Overall, the performances of the proposed deep learning-based prediction models were comparable.

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A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea

Atmosphere ◽

10.3390/atmos11040348 ◽

2020 ◽

Vol 11 (4) ◽

pp. 348 ◽

Cited By ~ 3

Author(s):

Guang Yang ◽

HwaMin Lee ◽

Giyeol Lee

Keyword(s):

Air Pollution ◽

Deep Learning ◽

Short Term Memory ◽

Hybrid Models ◽

Short Term ◽

Learning Methods ◽

Term Memory ◽

Long Short Term Memory ◽

Gated Recurrent Unit ◽

Concentration Levels

Both long- and short-term exposure to high concentrations of airborne particulate matter (PM) severely affect human health. Many countries now regulate PM concentrations. Early-warning systems based on PM concentration levels are urgently required to allow countermeasures to reduce harm and loss. Previous studies sought to establish accurate, efficient predictive models. Many machine-learning methods are used for air pollution forecasting. The long short-term memory and gated recurrent unit methods, typical deep-learning methods, reliably predict PM levels with some limitations. In this paper, the authors proposed novel hybrid models to combine the strength of two types of deep learning methods. Moreover, the authors compare hybrid deep-learning methods (convolutional neural network (CNN)—long short-term memory (LSTM) and CNN—gated recurrent unit (GRU)) with several stand-alone methods (LSTM, GRU) in terms of predicting PM concentrations in 39 stations in Seoul. Hourly air pollution data and meteorological data from January 2015 to December 2018 was used for these training models. The results of the experiment confirmed that the proposed prediction model could predict the PM concentrations for the next 7 days. Hybrid models outperformed single models in five areas selected randomly with the lowest root mean square error (RMSE) and mean absolute error (MAE) values for both PM10 and PM2.5. The error rate for PM10 prediction in Gangnam with RMSE is 1.688, and MAE is 1.161. For hybrid models, the CNN–GRU better-predicted PM10 for all stations selected, while the CNN–LSTM model performed better on predicting PM2.5.

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Feature selection using C4.5 algorithm for electricity price prediction

2014 International Conference on Machine Learning and Cybernetics ◽

10.1109/icmlc.2014.7009113 ◽

2014 ◽

Cited By ~ 2

Author(s):

Hehui Qian ◽

Zhiwei Qiu

Keyword(s):

Feature Selection ◽

Electricity Price ◽

Price Prediction ◽

C4.5 Algorithm

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Bitcoin Price Prediction Based on Deep Learning Methods

Journal of Mathematical Finance ◽

10.4236/jmf.2020.101009 ◽

2020 ◽

Vol 10 (01) ◽

pp. 132-139

Author(s):

Xiangxi Jiang

Keyword(s):

Deep Learning ◽

Learning Methods ◽

Price Prediction

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Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches

Applied Sciences ◽

10.3390/app11104499 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4499

Author(s):

Mei-Ling Huang ◽

Yun-Zhi Li

Keyword(s):

Neural Network ◽

Machine Learning ◽

Feature Selection ◽

Deep Learning ◽

Prediction Accuracy ◽

Major League Baseball ◽

Support Vector ◽

Learning Methods ◽

Machine Learning Methods ◽

Major League

Major League Baseball (MLB) is the highest level of professional baseball in the world and accounts for some of the most popular international sporting events. Many scholars have conducted research on predicting the outcome of MLB matches. The accuracy in predicting the results of baseball games is low. Therefore, deep learning and machine learning methods were used to build models for predicting the outcomes (win/loss) of MLB matches and investigate the differences between the models in terms of their performance. The match data of 30 teams during the 2019 MLB season with only the starting pitcher or with all pitchers in the pitcher category were collected to compare the prediction accuracy. A one-dimensional convolutional neural network (1DCNN), a traditional machine learning artificial neural network (ANN), and a support vector machine (SVM) were used to predict match outcomes with fivefold cross-validation to evaluate model performance. The highest prediction accuracies were 93.4%, 93.91%, and 93.90% with the 1DCNN, ANN, SVM models, respectively, before feature selection; after feature selection, the highest accuracies obtained were 94.18% and 94.16% with the ANN and SVM models, respectively. The prediction results obtained with the three models were similar, and the prediction accuracies were much higher than those obtained in related studies. Moreover, a 1DCNN was used for the first time for predicting the outcome of MLB matches, and it achieved a prediction accuracy similar to that achieved by machine learning methods.

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Enhanced Real-time Electricity Price Prediction with a Novel Feature Selection Technique

2019 IEEE Sustainable Power and Energy Conference (iSPEC) ◽

10.1109/ispec48194.2019.8975186 ◽

2019 ◽

Author(s):

Chenyang Huang ◽

Zheng Yan ◽

Jingyang Yun ◽

Le Xie ◽

Yuanyuan Li

Keyword(s):

Feature Selection ◽

Real Time ◽

Electricity Price ◽

Feature Selection Technique ◽

Selection Technique ◽

Price Prediction

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House price prediction based on machine learning and deep learning methods

10.1109/eiecs53707.2021.9587907 ◽

2021 ◽

Author(s):

Yihao Chen ◽

Runtian Xue ◽

Yu Zhang

Keyword(s):

Machine Learning ◽

Deep Learning ◽

House Price ◽

Learning Methods ◽

Price Prediction

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An Enhancement of Feature Selection Algorithm for EDM: A Review

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i5.661 ◽

2018 ◽

Vol 8 (5) ◽

pp. 29

Author(s):

Manpreet Kaur ◽

Chamkaur Singh

Keyword(s):

Feature Selection ◽

Educational Data Mining ◽

Problem Formulation ◽

Research Area ◽

Education Quality ◽

Educational Institutions ◽

Selection Algorithm ◽

Positive Role ◽

Data Set ◽

Selection Algorithms

Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. Feature Selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. .In this papers the different problems that are defined in problem formulation. All these problems are resolved in future. Furthermore the paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention.

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