Neural Networks for Cryptocurrency Evaluation and Price Fluctuation Forecasting

Christoforou, Emmanouil; Emiris, Ioannis Z.; Florakis, A.

doi:10.1007/978-3-030-37110-4_10

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…Long Short-Term Memory (LSTM) represents a specialized recurrent neural network (RNN) architecture meticulously crafted to grasp and model long-term dependencies inherent in sequential data (Christoforou et al, 2020). Unlike conventional RNNs, LSTM networks integrate dedicated memory cells and gating mechanisms, addressing the prevalent issue of vanishing gradients that often obstructs deep neural network training on lengthy sequences.…”

Section: Lstm (Long Short-term Memory)mentioning

confidence: 99%

Forecasting Bitcoin Prices: A Comparative Study of Machine Learning and Deep Learning Algorithms

Alizadegan,

Radmehr,

Ilani

2024

Preprint

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The cryptocurrency market, particularly Bitcoin, has witnessed significant volatility, making accurate price prediction a challenging yet crucial task. This research explores the application of four powerful machine learning algorithms), Light Gradient Boosting Machine (LightGBM , Long Short Term Memory (LSTM), Bidirectional Long Short Term Memory (BiLSTM) and Extreme Gradient Boosting (XGBoost), for forecasting Bitcoin prices. The study focuses on evaluating the predictive performance using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) as the evaluation metrics. The LSTM and Bi-LSTM, a type of recurrent neural network (RNN), are known for that ability to capture long-term dependencies in time series data. On the other hand, LightGBM and XGBoost, a gradient boosting framework, excels in handling large datasets efficiently and delivering accurate predictions. By employing these algorithms, this research aims to enhance the accuracy of Bitcoin price predictions compared to traditional methods. The experimental setup involves training and validating the models on historical Bitcoin price data. The MAE and RMSE metrics are utilized to assess the models' predictive accuracy, providing a comprehensive evaluation of their performance. The comparative analysis of machine learning models sheds light on their strengths and weaknesses in the context of cryptocurrency price prediction. The results showcase the importance of employing advanced machine learning techniques in forecasting financial time series, highlighting the potential for improved decision-making in cryptocurrency trading and investment strategies.

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Section: Lstm (Long Short-term Memory)mentioning

confidence: 99%

Forecasting Bitcoin Prices: A Comparative Study of Machine Learning and Deep Learning Algorithms

Alizadegan,

Radmehr,

Ilani

2024

Preprint

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“…DSTNN combines CNN and RNN, in order to capture, respectively, geospatial and temporal aspects of wind. The use of RNN was inspired by the their success in capturing general time-series [3]. Our model avoids actual measurements but relies on WRF predictions provided by a two-nested scheme, with increased resolution in the rectangle of interest (Figure 1).…”

Section: Our Contributionsmentioning

confidence: 99%

Spatio-temporal deep learning for day-ahead wind speed forecasting relying on WRF predictions

et al. 2021

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We focus on deep learning algorithms, improving upon the Weather Research and Forecasting (WRF) model, and we show that the combination of these methods produces day-ahead wind speed predictions of high accuracy, with no need for previous-day measurements. We also show that previous-day data, if available, offer a significant enhancement. Our main contribution is the design and testing of original neural networks that capture both spatial and temporal characteristics of the wind, by combining convolutional (CNN) as well as recurrent (RNN) neural networks. The input predictions are obtained by a WRF model that we appropriately parameterize; we also specify a grid adapted to each park so as to capture its topography. Training uses historical data from 5 wind farms in Greece, and the 5-month testing period includes winter months, which exhibit the highest wind speed values. Our models improve WRF accuracy on average by 19.4%, and the improvement occurs in every month; expectedly, the improvement is lowest for the park where WRF performs best. Our neural network is competitive to state-of-the-art models, achieving an average MAE of 1.75 m/s. Accuracy improves for speed values up to 20 m/s, which are important in wind energy prediction. We also develop

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“…We expect that the frameworks and the computational tools we present in the sequel can be generalized and used to handle further problems in fintech. For example, they could be combined with various asset-pricing models and methods to predict assets' returns by Machine Learning and AI methods [6]. Additionally, we believe that the new score can be used to define new performance measures and optimal portfolios according to these measures.…”

Section: Contributionsmentioning

confidence: 99%

Modeling asset allocation strategies and a new portfolio performance score

Chalkis,

Christoforou,

Emiris

et al. 2020

Preprint

Self Cite

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We discuss a powerful, geometric representation of financial portfolios and stock markets, which identifies the space of portfolios with the points lying in a simplex convex polytope. The ambient space has dimension equal to the number of stocks, or assets. Although our statistical tools are quite general, in this paper we focus on the problem of portfolio scoring. Our contribution is to introduce an original computational framework to model portfolio allocation strategies, which is of independent interest for computational finance. To model asset allocation strategies, we employ log-concave distributions centered on portfolio benchmarks. Our approach addresses the crucial question of evaluating portfolio management, and is relevant to the individual private investors as well as financial organizations. We evaluate the performance of an allocation, in a certain time period, by providing a new portfolio score, based on the aforementioned framework and concepts. In particular, it relies on the expected proportion of actually invested portfolios that it outperforms when a certain set of strategies take place in that time period. We also discuss how this set of strategies -and the knowledge one may have about themcould vary in our framework, and we provide additional versions of our score in order to obtain a more complete picture of its performance. In all cases, we show that the score computations can be performed efficiently. Last but not least, we expect this framework to be useful in portfolio optimization and in automatically identifying extreme phenomena in a stock market.

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Neural Networks for Cryptocurrency Evaluation and Price Fluctuation Forecasting

Cited by 9 publications

References 14 publications

Forecasting Bitcoin Prices: A Comparative Study of Machine Learning and Deep Learning Algorithms

Forecasting Bitcoin Prices: A Comparative Study of Machine Learning and Deep Learning Algorithms

Spatio-temporal deep learning for day-ahead wind speed forecasting relying on WRF predictions

Modeling asset allocation strategies and a new portfolio performance score

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