Prediction of Ether Prices Using DeepAR and Probabilistic Forecasting

Ferenczi, Andras; Bǎdicǎ, Costin

doi:10.1007/978-3-031-08754-7_73

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…The XG-Boostbased model has also shown good performance in previous studies on this domain [25,29]. Several studies have investigated the use of Mempool data, investigation of these data toward improving forecasting performance and price dynamic understanding is also of interest [28,29,34,40].…”

Section: Future Workmentioning

confidence: 90%

“…Ferenczi and Bădică [40] investigated the prediction of Ethereum gas price with Amazon SageMaker DeepAR [41] and found that the choice of covariates had a large effect on model performance. They found that gas prices were impacted by various factors viz including seasonality, volume of transactions, transaction values, number of token transactions and amount of gas used per block.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

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Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods

Butler

Crane

2023

Mathematics

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Gas is the transaction-fee metering system of the Ethereum network. Users of the network are required to select a gas price for submission with their transaction, creating a risk of overpaying or delayed/unprocessed transactions involved in this selection. In this work, we investigate data in the aftermath of the London Hard Fork and shed insight into the transaction dynamics of the network after this major fork. As such, this paper provides an update on work previous to 2019 on the link between EthUSD/BitUSD and gas price. For forecasting, we compare a novel combination of machine learning methods such as Direct-Recursive Hybrid LSTM, CNN-LSTM, and Attention-LSTM. These are combined with wavelet threshold denoising and matrix profile data processing toward the forecasting of block minimum gas price, on a 5-min timescale, over multiple lookaheads. As the first application of the matrix profile being applied to gas price data and forecasting that we are aware of, this study demonstrates that matrix profile data can enhance attention-based models; however, given the hardware constraints, hybrid models outperformed attention and CNN-LSTM models. The wavelet coherence of inputs demonstrates correlation in multiple variables on a 1-day timescale, which is a deviation of base free from gas price. A Direct-Recursive Hybrid LSTM strategy is found to outperform other models, with an average RMSE of 26.08 and R2 of 0.54 over a 50-min lookahead window compared to an RMSE of 26.78 and R2 of 0.452 in the best-performing attention model. Hybrid models are shown to have favorable performance up to a 20-min lookahead with performance being comparable to attention models when forecasting 25–50-min ahead. Forecasts over a range of lookaheads allow users to make an informed decision on gas price selection and the optimal window to submit their transaction in without fear of their transaction being rejected. This, in turn, gives more detailed insight into gas price dynamics than existing recommenders, oracles and forecasting approaches, which provide simple heuristics or limited lookahead horizons.

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Section: Future Workmentioning

confidence: 90%

Section: Deep Learning Modelsmentioning

confidence: 99%

Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods

Butler

Crane

2023

Mathematics

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“…(6)DeepAR [29]: Autoregressive RNN model for probabilistic forecasting. (7)WaveNet [30]: autoregressive deep NN model combining causal filters with dilated convolutions.…”

Section: Deep Learning Model：mentioning

confidence: 99%

Enhanced ES-adRNNe Load Forecasting With Contextual Augmentation on Similar Load Days

Wang,

Wu,

et al. 2023

IEEE Access

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The importance of accurate prediction of power load variations for ensuring the reliability and rationality of power supply cannot be overstated. The GCES-adRNNe (Grey Relational Analysis Contextually Enhanced ES-adRNNe) is proposed as a load forecasting model that incorporates context augmentation on similar load days. Firstly, the selection of similar load days as representative sequences using the comprehensive grey correlation analysis method is enforced. Furthermore, context information is extracted from the representative sequences, and the individual sequences are dynamically adjusted to accommodate the main track prediction using XGBoost. Moreover, a stacked recurrent neural network (RNN) architecture with multiple layers is used. It incorporates attention-enhanced gated recurrent units (GRU) to capture various dependencies in the time series, such as short-term, long-term, and seasonal dependencies. The GRU also dynamically weight input information. Finally, the prediction results are post-processed. Experimental results indicate that the proposed model yields improvements in terms of RMSE, MAPE, StdPE, and other aspects compared to other load forecasting models. It is indicated that the model, when considering similar load days, is capable of capturing the trends and characteristics of electricity load changes more accurately, thereby providing strong support for ensuring the reliability and rationality of power supply.

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Prediction of Ether Prices Using DeepAR and Probabilistic Forecasting

Cited by 2 publications

References 9 publications

Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods

Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods

Enhanced ES-adRNNe Load Forecasting With Contextual Augmentation on Similar Load Days

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