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A Deep Learning-Based Cryptocurrency Price Prediction Model That Uses On-Chain Data

IEEE ACCESS 2022年 10卷 56232-56248页

作者： Kim, Gyeongho Shin, Dong-Hyun Choi, Jae Gyeong Lim, Sunghoon Ulsan Natl Inst Sci & Technol Dept Ind Engn Ulsan 44919 South Korea Ulsan Natl Inst Sci & Technol Dept Biomed Engn Ulsan 44919 South Korea Ulsan Natl Inst Sci & Technol Inst Ind Revolut 4 Ulsan 44919 South Korea

Cryptocurrency has recently attracted substantial interest from investors due to its underlying philosophy of decentralization and transparency. Considering cryptocurrency's volatility and unique characteristics, accurate price prediction is essential for developing successful investment strategies. To this end, the authors of this work propose a novel framework that predicts the price of Bitcoin (BTC), a dominant cryptocurrency. For stable prediction performance in unseen price range, the change point detection technique is employed. In particular, it is used to segment time-series data so that normalization can be separately conducted based on segmentation. In addition, on-chain data, the unique records listed on the blockchain that are inherent in cryptocurrencies, are collected and utilized as input variables to predict prices. Furthermore, this work proposes self-attention-based multiple long short-term memory (SAM-LSTM), which consists of multiple LSTM modules for on-chain variable groups and the attention mechanism, for the prediction model. Experiments with real-world BTC price data and various method setups have proven the proposed framework's effectiveness in BTC price prediction. The results are promising, with the highest MAE, RMSE, MSE, and MAPE values of 0.3462, 0.5035, 0.2536, and 1.3251, respectively.

关键词： Cryptocurrency Blockchains Predictive models Investment Bitcoin Gold Data models Blockchain cryptocurrency Bitcoin deep learning prediction methods change detection algorithms

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Bayesian Selection for the l₂-Potts Model Regularization Parameter: 1-D Piecewise Constant Signal Denoising

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2017年第19期65卷 5215-5224页

作者： Frecon, Jordan Pustelnik, Nelly Dobigeon, Nicolas Wendt, Herwig Abry, Patrice Univ Claude Bernard Univ Lyon ENS Lyon CNRSLab Phys F-69342 Lyon France Univ Toulouse IRIT INP ENSEEIHT CNRS Toulouse France

Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures. The former lead to low computational time but require the selection of a regularization parameter, whose value significantly impacts the achieved solution, and whose automated selection remains an involved and challenging problem. Conversely, fully Bayesian formalisms encapsulate the regularization parameter selection into hierarchical models, at the price of high computational costs. This contribution proposes an operational strategy that combines hierarchical Bayesian and Potts model formulations, with the double aim of automatically tuning the regularization parameter and maintaining computational efficiency. The proposed procedure relies on formally connecting a Bayesian framework to a l(2)-Potts functional. Behaviors and performance for the proposed piecewise constant denoising and regularization parameter tuning techniques are studied qualitatively and assessed quantitatively, and shown to compare favorably against those of a fully Bayesian hierarchical procedure, both in accuracy and computational load.

关键词： change detection algorithms time series analysis Bayes methods

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On-board processor for direct distribution of change detection data products [radar imaging]

On-board processor for direct distribution of change detecti...

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IEEE National Conference on Radar

作者： Y. Lou S. Hensley C. Le D. Moller Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA

We are developing an on-board imaging radar data processor for repeat-pass change detection and hazard management. This is the enabling technology for NASA ESE to utilize imaging radars. This processor enables the observation and use of surface deformation data over rapidly evolving natural hazards, both as an aid to scientific understanding and to provide timely data to agencies responsible for the management and mitigation of natural disasters. Many hazards occur over periods of hours to days, and need to be sampled quickly. The new technology has the potential to save many lives and millions of dollars by putting critical information in the hands of disaster management agencies in time to be of use. The processor architecture integrates two key technologies by combining a field programmable gate array (FPGA) front-end with a reconfigurable computing back-end. A searchable on-board data archive stores the reference data sets needed for the change detection processing. In this paper, we present an overview of the change detection processing algorithm and the preliminary hardware architecture.

关键词： Radar detection Radar imaging Hazards Computer architecture Field programmable gate arrays Space technology NASA Disaster management change detection algorithms Hardware

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A fast scene change detection on MPEG coding parameter domain

A fast scene change detection on MPEG coding parameter domai...

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IEEE International Conference on Image Processing

作者： M. Sugano Y. Nakajima H. Yanagihara A. Yoneyama KDD R&D Laboratories Japan

This paper proposes a very fast and accurate scene change detection algorithm on MPEG coding parameter domain. The fast operation can be obtained by spatio-temporally subsampling coding information and by exploiting only coding parameters extracted in variable length decoding (VLD) stage, while the accurate detection is accomplished by examining the statistical characteristics of various scene changes on the coding parameter domain. The computer simulation shows that the proposed algorithm can accomplish detection more than 5 times faster than that of real-time playback for MPEG-2 video sequences using a standard workstation. It is also shown that most of abrupt scene changes, dissolve transitions, and wipe transitions have been successfully detected.

关键词： Layout change detection algorithms Decoding detection algorithms Motion detection Discrete cosine transforms Research and development Video sequences Workstations Gunshot detection systems

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Remote Sensing Image change detection Based on Swarm Intelligent Algorithm

Remote Sensing Image Change Detection Based on Swarm Intelli...

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International Conference on Multimedia Technology

作者： Qin Dai Jianbo Liu Shibin Liu Center for Earth Observation and Digital Earth CAS Beijing China

ISBN: (纸本)9781424478712

For several decades the remote sensing change detection methods for depicting land cover have gained a great achievements, and the artificial intelligence techniques have being applied to remote sensing data processing and show great potential. Ant colony optimization (ACO) and Particle swarm optimization (PSO) as the two main algorithms of swarm intelligence, and because of the self-organization, cooperation, communication and other intelligent merits, they have great potential in research area. This paper introduces remotely sensed image change detection using the swarm intelligent algorithm. Selecting Landsat-5 TM image located in Beijing area as experimental data, this paper use the algorithm for constructing the change rules, then use these rules to process the example data, the experiment results show that warm intelligent algorithm has provided a new method for remote sensing image change detection.

关键词： Remote sensing Classification algorithms change detection algorithms Earth Particle swarm optimization Training data Data mining

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Investigation of multiscale product for change detection in difference images

Investigation of multiscale product for change detection in ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： M. Beauchemin K.B. Fung Canada Centre for Remote Sensing Natural Resources Canada Ottawa Canada

We investigate the potential of multiscale product as a measure for change detection in difference images. The rationale relies on exploiting the cross-scale correlation characteristics of signal and noise. We use Monte Carlo simulations to assess the detection and localization performances of the multiscale product for the detection of linear features of different widths. Comparisons are made with change detection based on single scale as well as other multiscale combination rules. Preliminary simulation results show that, overall, a good performance balance is obtained for change detection over the range of SNRs and line widths investigated.

关键词： Pixel Image edge detection Signal to noise ratio Computer vision Remote sensing Image analysis Acoustic noise Noise reduction Algorithm design and analysis change detection algorithms

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detection of Speaker change Points in Conversational Speech

Detection of Speaker Change Points in Conversational Speech

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IEEE Conference on Aerospace

作者： Michael A. Carlin Brett Y. Smolenski Air Force Research Laboratory IFEC Rome NY USA Research Associates for Defense Conversion Inc. Marcy NY USA

An important preprocessing step in many automatic speech segmentation and speaker clustering systems is the accurate detection of speaker change points, the times when one speaker stops talking and another begins. However, this becomes very difficult in conversational speech since utterance lengths can be extremely short, speaker changes occur frequently, speakers may talk over one another (co-channel interference), and the recording environment and/or communication channel is sub-optimal or degraded. Modern aviation systems can benefit from this research as a pre-processing stage in a variety of applications. Examples include automatic segmentation and clustering of pilot/air traffic controller communications, detection of a third or unauthorized speaker in commercial airline cockpits, and automatic transcription of cockpit audio recordings. This research presents an approach to detecting speaker change points using information obtained from voiced speech segments. This permits taking advantage of the facts that (1) speaker starting and stopping information should be contained between segments of voiced speech and (2) voiced speech contains the most useful speaker identifiable information. The technique presented here shows promise as an enhancement to currently available change point detection algorithms.

关键词： Speech Interchannel interference change detection algorithms Loudspeakers Communication channels Degradation Air traffic control Humans Laboratories Audio recording

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change detection over Tropical Peat Swamp Forests in Indonesia Using SAR Time-Series Data

Change Detection over Tropical Peat Swamp Forests in Indones...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Anam Sabir Unmesh Khati Marco Lavalle DAASE Indian Institute of Technology Indore India NASA-Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Monitoring forest ecosystems requires both, continuous time-series datasets and a comprehensive method for analyzing the same. Synthetic Aperture RADAR (SAR) data aids the processing by maintaining an uninterrupted time-series dataset. The backscatter derived from SAR data is closely related to the changes taking place in the targeted area. Therefore the changes and disturbances taking place in the forests results in corresponding variations in the backscatter values. These temporal signatures of backscatter can be used for monitoring forests with statistical change detection algorithms. In this study, Cumulative Sums of change (CuSum) and Sequential Omnibus algorithms are used with Sentinel-1 SAR data for deforestation mapping in Central Kalimantan, Indonesia. CuSum showed an overall better performance than the sequential omnibus algorithm. It was observed that the drop in backscatter on account of deforestation was significant in case of both polarization channels. However, cross-pol channel showed better results with an average overall accuracy of 0.74.

关键词： Deforestation Accuracy change detection algorithms Shape Noise Sentinel-1 Peat

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Latency change detection in evoked potentials by direct least mean p-norm adaptive time delay estimation

Latency change detection in evoked potentials by direct leas...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Tianshuang Qiu Xuan Kong Department of Electrical Engineering Northern Illinois University De Kalb IL USA

Evoked potentials (EP) have been widely used to quantify neurological system properties. changes in EP latency may be linked to neurological injuries. Many traditional EP analysis methods are developed under the condition that the additive background EEG noises are Gaussian. This paper proposes a latency change detection and estimation algorithm under the /spl alpha/-stable noise condition, a generalization of Gaussian noise assumption. An analysis shows that the /spl alpha/-stable model fits the noises from an impact acceleration experiment better than the Gaussian model. The robustness of the proposed algorithm is demonstrated through computer simulations and experimental data analysis, while traditional time delay estimation algorithm fails to yield an accurate estimate of the latency changes under lower-order /spl alpha/-stable noise conditions.

关键词： Gaussian noise change detection algorithms Brain modeling Delay estimation Yield estimation Injuries Electroencephalography Additive noise Background noise Acceleration

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Real-time automatic sea-floor change detection from video

Real-time automatic sea-floor change detection from video

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OCEANS

作者： K. Lebart E. Trucco D.M. Lane Oceans Systems Laboratory Department of Computing and Electrical Egineering Heriot-Watt University Edinburgh UK

It is often the case that only sparse sequences of videos from scientific underwater surveys actually contain important information for the expert. A system automatically detecting those critical parts, particularly during the post-mission tape analysis, would alleviate the expert work load and improve data exploitation. The authors present a novel set of algorithms to detect in real time significant context changes in benthic videos. The detectors presented rely on an unsupervised image classification scheme: the time changes in the image contents are analyzed in the feature space. The algorithms are explained in detail, and experimental results with real underwater images reported. Various issues related to the complexity of the problem of automatically analysing underwater videos are also discussed.

关键词： Cameras Videoconference Streaming media Geology Oceans Image analysis change detection algorithms Underwater tracking Information analysis Underwater vehicles

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