Machine learning books and papers

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Title
A Comparative Analysis of Machine Learning Models on Cryptocurrency Encompassing Indicators of Gold, Dollar, And Technical Indicators
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Short title
Time series forecasting, ML, Gradient Boost Machine, BTC, cryptocurrency.
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Abstract
In recent years, the application of machine learning models in financial forecasting has gained significant traction due to their ability to capture complex patterns in diverse datasets. This study presents a comprehensive comparison of several prominent machine learning algorithms, including XGBoost, AdaBoost, CatBoost, Random Forest, Decision Trees and LightGBM, across different datasets encompassing indicators of gold, dollar, and technical indicators. The evaluation is conducted on a range of performance metrics to ascertain the efficacy of each model in predicting financial trends and fluctuations. Through ML analysis, we examine the models' capabilities in handling the unique characteristics and dynamics inherent in each dataset, providing insights into their relative strengths and weaknesses. Furthermore, this research contributes to the existing literature by offering a comparative framework for assessing the suitability of machine learning algorithms in financial forecasting tasks. The findings of this study have implications for practitioners and researchers seeking to employ machine learning techniques in financial markets, aiding in informed decision-making and risk management strategies.
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Field
Business, Marketing, Industrial Engineering, Computer Engineering.
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journal
1. Annals of Operations Research (7.1 CiteScore, 4.8 Impact Factor)
2. Neural Computing and Applications ( 8.7 CiteScore, 6.0 Impact Factor)
3. IEEE Access (9.0 CiteScore, 3.9 Impact Factor)

با عرض سلام نفرات اول و دوم این مقاله رو خالی داریم . دوستانی که نیاز دارن با بنده هماهنگ کنند.

▶️ @Raminmousa

@Machine_learn

@Paper4money

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5.9K viewsApr 18, 2024 at 07:06

Title
A Comparative Analysis of Machine Learning Models on Cryptocurrency Encompassing Indicators of Gold, Dollar, And Technical Indicators
————————————
Short title
Time series forecasting, ML, Gradient Boost Machine, BTC, cryptocurrency.
————————————-
Abstract
In recent years, the application of machine learning models in financial forecasting has gained significant traction due to their ability to capture complex patterns in diverse datasets. This study presents a comprehensive comparison of several prominent machine learning algorithms, including XGBoost, AdaBoost, CatBoost, Random Forest, Decision Trees and LightGBM, across different datasets encompassing indicators of gold, dollar, and technical indicators. The evaluation is conducted on a range of performance metrics to ascertain the efficacy of each model in predicting financial trends and fluctuations. Through ML analysis, we examine the models' capabilities in handling the unique characteristics and dynamics inherent in each dataset, providing insights into their relative strengths and weaknesses. Furthermore, this research contributes to the existing literature by offering a comparative framework for assessing the suitability of machine learning algorithms in financial forecasting tasks. The findings of this study have implications for practitioners and researchers seeking to employ machine learning techniques in financial markets, aiding in informed decision-making and risk management strategies.
—————————————
Field
Business, Marketing, Industrial Engineering, Computer Engineering.
——————————————
journal
1. Annals of Operations Research (7.1 CiteScore, 4.8 Impact Factor)
2. Neural Computing and Applications ( 8.7 CiteScore, 6.0 Impact Factor)
3. IEEE Access (9.0 CiteScore, 3.9 Impact Factor)

با عرض سلام نفرات اول و دوم این مقاله رو خالی داریم . دوستانی که نیاز دارن با بنده هماهنگ کنند.

▶️ @Raminmousa

@Machine_learn

@Paper4money