Comprehensive Modeling Approaches for Forecasting Bitcoin Transaction Fees: A Comparative Study

• URL: http://arxiv.org/abs/2502.01029v1
• Date: Mon, 03 Feb 2025 03:52:07 GMT
• Title: Comprehensive Modeling Approaches for Forecasting Bitcoin Transaction Fees: A Comparative Study
• Authors: Jiangqin Ma, Erfan Mahmoudinia,
• Abstract summary: This study presents a systematic evaluation of six predictive models for forecasting Bitcoin transaction fees. Our approach integrates comprehensive feature engineering spanning mempool metrics, network parameters, and historical fee patterns. Traditional statistical approaches outperform more complex deep learning architectures.
• Score: 0.0
• License:
• Abstract: Transaction fee prediction in Bitcoin's ecosystem represents a crucial challenge affecting both user costs and miner revenue optimization. This study presents a systematic evaluation of six predictive models for forecasting Bitcoin transaction fees across a 24-hour horizon (144 blocks): SARIMAX, Prophet, Time2Vec, Time2Vec with Attention, a Hybrid model combining SARIMAX with Gradient Boosting, and the Temporal Fusion Transformer (TFT). Our approach integrates comprehensive feature engineering spanning mempool metrics, network parameters, and historical fee patterns to capture the multifaceted dynamics of fee behavior. Through rigorous 5-fold cross-validation and independent testing, our analysis reveals that traditional statistical approaches outperform more complex deep learning architectures. The SARIMAX model achieves superior accuracy on the independent test set, while Prophet demonstrates strong performance during cross-validation. Notably, sophisticated deep learning models like Time2Vec and TFT show comparatively lower predictive power despite their architectural complexity. This performance disparity likely stems from the relatively constrained training dataset of 91 days, suggesting that deep learning models may achieve enhanced results with extended historical data. These findings offer significant practical implications for cryptocurrency stakeholders, providing empirically-validated guidance for fee-sensitive decision making while illuminating critical considerations in model selection based on data constraints. The study establishes a foundation for advanced fee prediction while highlighting the current advantages of traditional statistical methods in this domain.

Related papers

• Comparative Study of Bitcoin Price Prediction [0.0]
  We employ five-fold cross-validation to enhance generalization and utilize L2 regularization to reduce overfitting and noise. Our study demonstrates that the GRUs models offer better accuracy than LSTMs model for predicting Bitcoin's price.
  arXiv  Detail & Related papers  (2024-05-13T18:10:34Z)
• Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach [2.0213537170294793]
  This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting.
  arXiv  Detail & Related papers  (2023-12-31T16:38:32Z)
• The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting [50.48888534815361]
  We show that models trained with the Channel Independent (CI) strategy outperform those trained with the Channel Dependent (CD) strategy. Our results conclude that the CD approach has higher capacity but often lacks robustness to accurately predict distributionally drifted time series. We propose a modified CD method called Predict Residuals with Regularization (PRReg) that can surpass the CI strategy.
  arXiv  Detail & Related papers  (2023-04-11T13:15:33Z)
• Bi-LSTM Price Prediction based on Attention Mechanism [2.455751370157653]
  We propose a bidirectional LSTM neural network based on an attention mechanism, which is based on two popular assets, gold and bitcoin. Using the forecast results, we achieved a return of 1089.34% in two years. We also compare the attention Bi-LSTM model proposed in this paper with the traditional model, and the results show that our model has the best performance in this data set.
  arXiv  Detail & Related papers  (2022-12-07T03:56:11Z)
• DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions [53.37679435230207]
  We propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data.
  arXiv  Detail & Related papers  (2022-09-23T16:13:47Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Forex Trading Volatility Prediction using Neural Network Models [6.09960572440709]
  We show how to construct the deep-learning network by the guidance of the empirical patterns of the intra-day volatility. The numerical results show that the multiscale Long Short-Term Memory (LSTM) model with the input of multi-currency pairs consistently achieves the state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2021-12-02T12:33:12Z)
• Sparse MoEs meet Efficient Ensembles [49.313497379189315]
  We study the interplay of two popular classes of such models: ensembles of neural networks and sparse mixture of experts (sparse MoEs) We present Efficient Ensemble of Experts (E$3$), a scalable and simple ensemble of sparse MoEs that takes the best of both classes of models, while using up to 45% fewer FLOPs than a deep ensemble.
  arXiv  Detail & Related papers  (2021-10-07T11:58:35Z)
• Pre-Trained Models: Past, Present and Future [126.21572378910746]
  Large-scale pre-trained models (PTMs) have recently achieved great success and become a milestone in the field of artificial intelligence (AI) By storing knowledge into huge parameters and fine-tuning on specific tasks, the rich knowledge implicitly encoded in huge parameters can benefit a variety of downstream tasks. It is now the consensus of the AI community to adopt PTMs as backbone for downstream tasks rather than learning models from scratch.
  arXiv  Detail & Related papers  (2021-06-14T02:40:32Z)
• Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning [109.74041512359476]
  We study a number of design decisions for the predictive model in visual MBRL algorithms. We find that a range of design decisions that are often considered crucial, such as the use of latent spaces, have little effect on task performance. We show how this phenomenon is related to exploration and how some of the lower-scoring models on standard benchmarks will perform the same as the best-performing models when trained on the same training data.
  arXiv  Detail & Related papers  (2020-12-08T18:03:21Z)
• A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data [20.392440676633573]
  This paper presents a framework that predicts price movement of cryptocurrencies from trade-by-trade data. The model is trained to achieve high performance on nearly a year of trade-by-trade data. In a realistic trading simulation setting, the prediction made by the model could be easily monetized.
  arXiv  Detail & Related papers  (2020-10-11T10:42:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.