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A Short-Term Cryptocurrency Price Movement Prediction Using Centrality Measures 使用中心性度量的短期加密货币价格走势预测
2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00058
Kin-Hon Ho, Wai-Han Chiu, Chin Li
{"title":"A Short-Term Cryptocurrency Price Movement Prediction Using Centrality Measures","authors":"Kin-Hon Ho, Wai-Han Chiu, Chin Li","doi":"10.1109/ICDMW51313.2020.00058","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00058","url":null,"abstract":"We conduct a network analysis with centrality measures, using historical daily close prices of top 120 cryptocurrencies between 2013 and 2020, to study and understand the dynamic evolution and characteristics of the cryptocurrency market. Our study has three primary findings: (1) the overall cross-return correlation among the cryptocurrencies is weakening from 2013 to 2016 and then strengthening thereafter; (2) cryptocurrencies that are primarily used for transaction payment, notably BTC, dominate the market until mid-2016, followed by those developed for applications using blockchain as the underlying technology, particularly data storage and recording such as MAID and FCT, between mid-2016 and mid-2017. Since then, ETH, alongside with its strongly correlated cryptocurrencies have replaced BTC to become the benchmark cryptocurrencies. Furthermore, during the outbreak of COVID-19, QTUM and BNB have intermittently replaced ETH to take the leading positions due to their active community engagement during the pandemic; (3) centrality measures are useful features in improving the prediction accuracy of the short-term cryptocurrency price movement.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114792531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Integration of Fuzzy and Deep Learning in Three-Way Decisions 模糊学习与深度学习在三方决策中的集成
2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00019
L. Subhashini, Yuefeng Li, Jinglan Zhang, Ajantha S Atukorale
{"title":"Integration of Fuzzy and Deep Learning in Three-Way Decisions","authors":"L. Subhashini, Yuefeng Li, Jinglan Zhang, Ajantha S Atukorale","doi":"10.1109/ICDMW51313.2020.00019","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00019","url":null,"abstract":"The problem of uncertainty is a challenging issue to solve in opinion mining models. Existing models that use machine learning algorithms are unable to identify uncertainty within online customer reviews because of broad uncertain boundaries. Many researchers have developed fuzzy models to solve this problem. However, the problem of large uncertain boundaries remains with fuzzy models. The common challenging issue is that there is a big uncertain boundary between positive and negative classes as user reviews (or opinions) include many uncertainties. Dealing with these uncertainties is problematic due in many frequently used words may be non-relevant. This paper proposes a three-way based framework which integrates fuzzy concepts and deep learning together to solve the problem of uncertainty. Many experiments were conducted using movie review and ebook review datasets. The experimental results show that the proposed three-way framework is useful for dealing with uncertainties in opinions and we were able to show that significant F-measure for two benchmark dataset.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114267495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Temporally-Reweighted Dirichlet Process Mixture Anomaly Detector 时间重加权狄利克雷过程混合异常检测器
2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00045
JunYong Tong, Nick Torenvliet
{"title":"Temporally-Reweighted Dirichlet Process Mixture Anomaly Detector","authors":"JunYong Tong, Nick Torenvliet","doi":"10.1109/ICDMW51313.2020.00045","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00045","url":null,"abstract":"This paper proposes a streaming anomaly detection algorithm using variational Bayesian non-parametric methods. We extend the use of Dirichlet process mixture models to anomaly detection for online streaming data through the use of streaming variational bayes method and a cohesion function. Using our algorithm, we were able to update model parameters sequentially near real-time, using a fixed amount of computational resources. The algorithm was able to capture the temporal dynamics of the data and enabled good online anomaly detection. We demonstrate the performance, and discuss results, of the algorithm on an industrial datasets with anomalies provided by a local utility.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130792155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
AttentionFM: Incorporating Attention Mechanism and Factorization Machine for Credit Scoring 基于注意力机制和因子分解机的信用评分
2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00056
Ying Liu, Wei Wang, Tianlin Zhang, Zhenyu Cui
{"title":"AttentionFM: Incorporating Attention Mechanism and Factorization Machine for Credit Scoring","authors":"Ying Liu, Wei Wang, Tianlin Zhang, Zhenyu Cui","doi":"10.1109/ICDMW51313.2020.00056","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00056","url":null,"abstract":"Learning effective feature interactions behind user behavior is challenging in credit scoring. Existing machine learning methods seem to have a strong bias towards low-order or high-order interactions, or require expertise feature engineering. In this paper, we present a novel neural network approach AttentionFM, which incorporates Factorization Machines and Attention mechanism for credit scoring. The proposed model focuses more on critical features and emphasizes both low- and high-order feature interactions, with no need of manually feature engineering on raw data representation. Experimental results demonstrate that our proposed model significantly outperforms the baselines based on two public datasets.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117273944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Detecting Dynamic Critical Links within Large Scale Network for Traffic State Prediction 基于流量状态预测的大规模网络动态关键链路检测
2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00119
Pierre-Antoine Laharotte, Romain Billot, Nour-Eddin El Faouzi
{"title":"Detecting Dynamic Critical Links within Large Scale Network for Traffic State Prediction","authors":"Pierre-Antoine Laharotte, Romain Billot, Nour-Eddin El Faouzi","doi":"10.1109/ICDMW51313.2020.00119","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00119","url":null,"abstract":"Can we expose the relationship between the physical dynamics of a network and its predictability? To contribute to this point, we propose a dimensionality reduction method for network states prediction based on spatiotemporal data. The method is intended to deal with large scale networks, where only a subset of critical links can be relevant for accurate multidimensional prediction (MIMO) performances. The algorithm is based on Latent Dirichlet Allocation (LDA) to highlight relevant topics in terms of networks dynamics. The feature selection trick relies on the assumption that the most representative links of the most dominant topics are critical links for short term prediction. The method is fully implemented to an original application field: short term road traffic prediction on large scale urban networks based on GPS data. Results highlight significant reductions in dimensionality and execution time, a global improvement of prediction performances as well as a better resilience to non recurrent traffic flow conditions.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121991379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Electric Energy Demand Forecasting with Explainable Time-series Modeling 基于可解释时间序列模型的电力需求预测
2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00101
Jin-Young Kim, Sung-Bae Cho
{"title":"Electric Energy Demand Forecasting with Explainable Time-series Modeling","authors":"Jin-Young Kim, Sung-Bae Cho","doi":"10.1109/ICDMW51313.2020.00101","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00101","url":null,"abstract":"Recently, deep learning models are utilized to predict the energy consumption. However, to construct the smart grid systems, the conventional methods have limitation on explanatory power or require manual analysis. To overcome it, in this paper, we present a novel deep learning model that can infer the predicted results by calculating the correlation between the latent variables and output as well as forecast the future consumption in high performance. The proposed model is composed of 1) a main encoder that models the past energy demand, 2) a sub encoder that models electric information except global active power as the latent variable in two dimensions, 3) a predictor that maps the future demand from the concatenation of the latent variables extracted from each encoder, and 4) an explainer that provides the most significant electric information. Several experiments on a household electric energy demand dataset show that the proposed model not only has better performance than the conventional models, but also provides the ability to explain the results by analyzing the correlation of inputs, latent variables, and energy demand predicted in the form of time-series.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"349 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122041042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
StreamDL: Deep Learning Serving Platform for AMI Stream Forecasting StreamDL: AMI流预测的深度学习服务平台
2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00104
Eunju Yang, Changha Lee, Ji-Hwan Kim, Tuan Manh Tao, Chan-Hyun Youn
{"title":"StreamDL: Deep Learning Serving Platform for AMI Stream Forecasting","authors":"Eunju Yang, Changha Lee, Ji-Hwan Kim, Tuan Manh Tao, Chan-Hyun Youn","doi":"10.1109/ICDMW51313.2020.00104","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00104","url":null,"abstract":"Advanced Metering Infrastructures (AMIs) facilitate individual load forecasting. The individual load forecasting not only improves the accuracy of aggregated load forecasting but is a fundamental component of various power applications. With the highlight of deep learning (DL) in the individual load forecasting, a serving platform specialized in deep learning is required to forecast with AMI stream data. However, the existing serving platforms for DL models do not consider stream data as an input but usually support image or text data through RESTful API. To solve this problem, we propose StreamDL that is a serving framework providing deep learning inference with AMI stream data. It leverages Apache Kafka to support stream data and Kubernetes to support the cloud environment. StreamDL considers the specific requirements for stream data, which supports stream parsing to fit any DL model especially recurrent network and continual training to alleviate accuracy degradation by the change of stream distribution. In this paper, we introduce the detail of the StreamDL platform and its use-cases using real AMI data.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127018210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Revenue Maximization using Multitask Learning for Promotion Recommendation 利用多任务学习实现推广推荐的收益最大化
2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00029
Venkataramana B. Kini, A. Manjunatha
{"title":"Revenue Maximization using Multitask Learning for Promotion Recommendation","authors":"Venkataramana B. Kini, A. Manjunatha","doi":"10.1109/ICDMW51313.2020.00029","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00029","url":null,"abstract":"This paper proposes and evaluates a multitask transfer learning approach to collectively optimize customer loyalty, retail revenue, and promotional revenue. Multitask neural network is employed to predict a customer's propensity to purchase within fine-grained categories. The network is then fine-tuned using transfer learning for a specific promotional campaign. Lastly, retail revenue and promotional revenue are jointly optimized conditioned on customer loyalty. Experiments are conducted using a large retail dataset that shows the efficacy of the proposed method compared to baselines used in the industry. A large retailer is currently adopting the proposed methodology in promotional campaigning owing to significant overall revenue and loyalty gains.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128935401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Learning Disentangled Representation of Residential Power Demand Peak via Convolutional-Recurrent Triplet Network 基于卷积-循环三重网络的住宅电力需求峰值解纠缠表征
2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00110
Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho
{"title":"Learning Disentangled Representation of Residential Power Demand Peak via Convolutional-Recurrent Triplet Network","authors":"Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho","doi":"10.1109/ICDMW51313.2020.00110","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00110","url":null,"abstract":"In the time-series models for predicting residential energy consumption, the energy properties collected through multiple sensors usually include irregular and seasonal factors. The irregular pattern resulting from them is called peak demand, which is a major cause of performance degradation. In order to enhance the performance, we propose a convolutional-recurrent triplet network to learn and detect the demand peaks. The proposed model generates the latent space for demand peaks from data, which is transferred into convolutional neural network-long short-term memory (CNN-LSTM) to finally predict the future power demand. Experiments with the dataset of UCI household power consumption composed of a total of 2,075,259 time-series data show that the proposed model reduces the error by 23.63% and outperforms the state-of-the-art deep learning models including the CNN-LSTM. Especially, the proposed model improves the prediction performance by modeling the distribution of demand peaks in Euclidean space.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122762712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Blockchain Applications to combat the global trade of falsified drugs 区块链应用于打击假药全球贸易
2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00127
Y. Kostyuchenko, Qingshan Jiang
{"title":"Blockchain Applications to combat the global trade of falsified drugs","authors":"Y. Kostyuchenko, Qingshan Jiang","doi":"10.1109/ICDMW51313.2020.00127","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00127","url":null,"abstract":"The globalization of the pharmaceutical supply chain has lead to new challenges, the leading position among them is the fight against falsified and substandard pharmaceutical products. Such kind of products causes ineffective or harmful therapies all over the world. Traditional centralized technical tools can hardly satisfy the requirements of the changing industry. In this paper, we research the application of Blockchain solutions to modernize the drug supply chain and minimize the amount of the poor-quality medications.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121258094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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