Best Feature Selection using Correlation Analysis for Prediction of Bitcoin Transaction Count

Ji, Se-Hyun; Baek, Ui-Jun; Mu-gon, Shin; Goo, Young-Hoon; Park, Jun-Sang; Kim, Myung-Sup

doi:10.23919/apnoms.2019.8892896

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…Overall sentiment data, as seen in Fig. 2.2 attributes, reveal a weak correlation concerning close price, which is on par with Ji [7] results, as shown in Fig. 3a.…”

Section: Correlation Analysis and Fregressionmentioning

confidence: 58%

“…Correlation analysis was conducted to understand the relationships between variables, using the Pearson correlation coe cient formula to quantify the strength and direction of these relationships [7]. Feature selection was then applied to re ne the model by identifying and retaining only those features with signi cant predictive power, thereby enhancing computational e ciency and reducing the risk of over tting [5].…”

Section: Data Preprocessing and Transformationmentioning

confidence: 99%

“…Our research endeavors to enrich the existing literature by providing a detailed examination of the predictive power of Google search trends and Twitter sentiment in the context of Bitcoin price uctuations. By employing a combination of traditional and innovative feature selection methodologies, such as Correlation Analysis, F-regression, and the application of Shapley values, this study aims to uncover novel insights into the factors driving Bitcoin's market dynamics [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Sentiment Analyis and Bitcoin Price Prediction

BAMIDELE

2024

Preprint

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The emergence of Bitcoin as a decentralized digital currency has underscored the importance of developing advanced techniques for predicting its price fluctuations. This study evaluates the predictive power of Bitcoin-related Google search volumes and Twitter sentiment analysis within short time frames. By leveraging machine learning algorithms and opinion mining, we identify correlations between online behaviors and Bitcoin price movements. Our methodology encompasses data sourcing, preprocessing, exploratory analysis, feature selection using Correlation Analysis, F-regression, Shapley values, and price prediction with a Long Short-Term Memory (LSTM) model. Findings reveal that Google search data, compared to Twitter sentiment, significantly enhances model accuracy and reduces prediction errors. The study suggests future research to investigate other search engines and online news sentiment, acknowledging limitations in data quality and accessibility of historical Twitter data.

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“…Overall sentiment data, as seen in Fig. 2.2 attributes, reveal a weak correlation concerning close price, which is on par with Ji [7] results, as shown in Fig. 3a.…”

Section: Correlation Analysis and Fregressionmentioning

confidence: 58%

Section: Data Preprocessing and Transformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sentiment Analyis and Bitcoin Price Prediction

BAMIDELE

2024

Preprint

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“…However, given the small size of data used in this study, we choose to focus on the Spearman correlation coefficient. Indeed, the correlation analysis has been proven to provide relevant features in the case of machine learning, see for instance [61]. The choice of the Spearman correlation rather than the Pearson is due to the fact that the relationship between the rheological parameters and the physico-chemical components is rather nonlinear.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

Data-driven polynomial chaos expansions for characterization of complex fluid rheology: Case study of phosphate slurry

Moçayd¹,

Seaı̈d

2021

Reliability Engineering & System Safety

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“…Although this method reduces the time to find parameter values, there is a limitation that the best combination of values cannot be found, since it does not take into account all possible combinations of the parameter values. Using the above method, a study predicted future bitcoin price with two hyperparameters, the length of the input sequence and the number of hidden layer units [12]. They found an optimal value of the number of hidden layer units.…”

Section: Motivationmentioning

confidence: 99%

Understanding Bitcoin Price Prediction Trends under Various Hyperparameter Configurations

Kim

Sung

2022

Computers

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Since bitcoin has gained recognition as a valuable asset, researchers have begun to use machine learning to predict bitcoin price. However, because of the impractical cost of hyperparameter optimization, it is greatly challenging to make accurate predictions. In this paper, we analyze the prediction performance trends under various hyperparameter configurations to help them identify the optimal hyperparameter combination with little effort. We employ two datasets which have different time periods with the same bitcoin price to analyze the prediction performance based on the similarity between the data used for learning and future data. With them, we measure the loss rates between predicted values and real price by adjusting the values of three representative hyperparameters. Through the analysis, we show that distinct hyperparameter configurations are needed for a high prediction accuracy according to the similarity between the data used for learning and the future data. Based on the result, we propose a direction for the hyperparameter optimization of the bitcoin price prediction showing a high accuracy.

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Best Feature Selection using Correlation Analysis for Prediction of Bitcoin Transaction Count

Cited by 5 publications

References 4 publications

Sentiment Analyis and Bitcoin Price Prediction

Sentiment Analyis and Bitcoin Price Prediction

Data-driven polynomial chaos expansions for characterization of complex fluid rheology: Case study of phosphate slurry

Understanding Bitcoin Price Prediction Trends under Various Hyperparameter Configurations

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