Wavelet Correlation Analysis of Geodetic Signals

Guo-qing, QU; Zhang, Bin; Su, Xinming

doi:10.1109/icnc.2009.322

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…Phase difference analysis is used to provide information on co-movement sign and lead-lag relationships [17]. Wavelets have application to a wide variety of time-series data, as shown by application to wideband power signals [16], widespread use in financial market studies [17] and Geodetic signals [18].…”

Section: Time Series Signal Processing and Data Miningmentioning

confidence: 99%

“…This paper is related to previous gas price forecasting and recommender work by Mars et al [14] and Werner et al [15]. The aim of this study is to first investigate the relation between potential model inputs in blockchain and exchange data, using wavelet coherence as seen Garrigan et al [16], Sun and Xu [17] and Qu et al [18]. The next stage is development of a forecasting model based on these inputs.…”

Section: Introductionmentioning

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: Time Series Signal Processing and Data Miningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods

Butler

Crane

2023

Mathematics

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“…Classical correlation detects a linear relationship in the frequency domain between two signals, while wavelet transform based correlation detects such similarities by taking into account both the time and the frequency domains, being a superior method [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

A novel method for single phase-to-ground fault location based on wavelet analysis and correlation

Bi¹,

Shen²,

Plans³

et al. 2013

2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT)

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This paper deals with the problem of single phaseto-ground fault location in distribution networks. A novel method is proposed that overcomes the difficulties that traditional fault location methods have, like their sensibility to the large transition resistance due to the fault. First, correlation is used in combination with the wavelet transform to analyze the series of transient electric wavefronts generated by the fault. Then, the travelling wave principle is applied to determine the distance between the busbar and the fault location. Theory analysis and ATP simulation indicate that the proposed method is robust and accurate.

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Wavelet Correlation Analysis of Geodetic Signals

Cited by 2 publications

References 8 publications

Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods

Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods

A novel method for single phase-to-ground fault location based on wavelet analysis and correlation

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