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A New Symbolization and Distance Measure Based Anomaly Mining Approach for Hydrological Time Series

Pengcheng Zhang, Yan Xiao, Yuelong Zhu, Jun Feng, Dingsheng Wan, Wenrui Li, Hareton Leung
2016 International Journal of Web Services Research  
The authors propose a new idea to solve the problem of hydrological anomaly mining based on time series.  ...  They propose Feature Points Symbolic Aggregate Approximation (FP_SAX) to improve the selection of feature points, and then measures the distance of strings by Symbol Distance based Dynamic Time Warping  ...  To solve these problems, this paper puts forward a new approach which is based on Extended Symbolic Aggregate Approximation (ESAX) (Lkhagva, Suzuki et al. 2006) and Dynamic Time Warping (DTW) (Müller  ... 
doi:10.4018/ijwsr.2016070102 fatcat:f74ybfp23bg2pk4a7y33egkvqu

Detecting Pattern Anomalies in Hydrological Time Series with Weighted Probabilistic Suffix Trees

Yufeng Yu, Dingsheng Wan, Qun Zhao, Huan Liu
2020 Water  
Experiments with different hydrological real-world time series are reported, and the results indicate that the proposed methods are fast and can correctly detect anomalous patterns for hydrological time  ...  series analysis, and thus promote the deep analysis and continuous utilization of hydrological time series data.  ...  Then, a new approach that is suitable for hydrological time series anomalous pattern detection is put forward, which makes the detection results accurate and efficient.  ... 
doi:10.3390/w12051464 fatcat:rqvsxaanqnfjpbejwxde6ecgta

Time-series data mining

Philippe Esling, Carlos Agon
2012 ACM Computing Surveys  
In this paper we intend to provide a survey of the techniques applied for time series data mining.  ...  We hope that this paper can provide a broad and deep understanding of the time series data mining research field.  ...  Jean Claude Lejosne, Professor of English for Special Purposes (ESP) for having improved the English wording of the manuscript.  ... 
doi:10.1145/2379776.2379788 fatcat:prjlpze5arefrkrnkrpsx3inke

A Novel Trend Symbolic Aggregate Approximation for Time Series [article]

Yufeng Yu, Yuelong Zhu, Dingsheng Wan, Qun Zhao, Huan Liu
2019 arXiv   pre-print
Symbolic Aggregate approximation (SAX) is a classical symbolic approach in many time series data mining applications.  ...  We also propose a modified distance measure by integrating the SAX distance with a weighted trend distance.  ...  approximation (SAX) is a classical symbolic the distance in the SAX representation and the Euclidean distance approach in many time series data mining applications.  ... 
arXiv:1905.00421v1 fatcat:wnhu335kmbditekqdlqcsvcxgy

Time Series Outlier Detection Based on Sliding Window Prediction

Yufeng Yu, Yuelong Zhu, Shijin Li, Dingsheng Wan
2014 Mathematical Problems in Engineering  
Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.  ...  a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns.  ...  Acknowledgments This work is supported by the Natural Science Foundation of China (nos. 51079040, 61170200, and 61370091) and the National Science and Technology Infrastructure of China (no. 2005DKA32000  ... 
doi:10.1155/2014/879736 fatcat:gvozebwylzfstevt6m5uysfy4a

Outlier Detection for Temporal Data: A Survey

Manish Gupta, Jing Gao, Charu C. Aggarwal, Jiawei Han
2014 IEEE Transactions on Knowledge and Data Engineering  
In the statistics community, outlier detection for time series data has been studied for decades.  ...  This has fueled the growth of different kinds of data sets such as data streams, spatiotemporal data, distributed streams, temporal networks, and time series data, generated by a multitude of applications  ...  Profile Similarity based Approaches These approaches maintain a normal profile and then compare a new time point against this profile to decide whether it is an outlier.  ... 
doi:10.1109/tkde.2013.184 fatcat:b6nableuvvgthlw3xxj6axabgi

Incorporating the spatio-temporal distribution of rainfall and basin geomorphology into nonlinear analyses of streamflow dynamics

Boyko Dodov, Efi Foufoula-Georgiou
2005 Advances in Water Resources  
The proposed framework is based on "hydrologically-relevant" rainfall-runoff phase-space reconstruction acknowledging the fact that rainfall-runoff is a stochastic spatially extended system rather than  ...  The methodology is applied to two basins in Central North America using 6-hour streamflow data and radar images for a period of five years.  ...  Acknowledgments This research was partially supported by a NASA/NOAA grant (NAG8-1519) and by an NSF grant (EAR-0120914) as part of the National Center for Earth Surface Dynamics (NCED) at the University  ... 
doi:10.1016/j.advwatres.2004.12.013 fatcat:pkfsyv6uujfffpvrvrds42bhtm

Medium and Long-Term Precipitation Forecasting Method Based on Data Augmentation and Machine Learning Algorithms

Tiantian Tang, Donglai Jiao, Tao Chen, Guan Gui
2022 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
This study improves the accuracy of precipitation forecast by expanding and balancing the information of sample data, and provides a new research idea for improving the accuracy of mediumand long-term  ...  In recent years, there are various methods for medium-and long-term precipitation forecasting based on machine learning algorithms.  ...  the information of sample data, and provides a new research idea for improving the accuracy of medium-and long-term hydrological forecasting.  ... 
doi:10.1109/jstars.2022.3140442 fatcat:kmhsm6h4gzc27gkx4e5glon3dy

Radar Interferometry as a Monitoring Tool for an Active Mining Area Using Sentinel-1 C-Band Data, Case Study of Riotinto Mine

Joaquin Escayo, Ignacio Marzan, David Martí, Fernando Tornos, Angelo Farci, Martin Schimmel, Ramon Carbonell, José Fernández
2022 Remote Sensing  
We propose a new method for combining ascending and descending results into a common dataset that provides better resolution.  ...  We demonstrate the great potential of the Sentinel-1 satellite as a complementary tool for monitoring systems in mining environments and we call for its use to be standardized to guarantee a safe and sustainable  ...  Acknowledgments: This work has been supported by Atalaya Mining facilitating access and logistics to the mine, as well as providing the necessary data to complete the study.  ... 
doi:10.3390/rs14133061 fatcat:2fbokl7hzfgb7izv3lc5myvvxi

The Impact of Precipitation Type Discrimination on Hydrologic Simulation: Rain–Snow Partitioning Derived from HMT-West Radar-Detected Brightband Height versus Surface Temperature Data

Naoki Mizukami, Victor Koren, Michael Smith, David Kingsmill, Ziya Zhang, Brian Cosgrove, Zhengtao Cui
2013 Journal of Hydrometeorology  
Continuous hourly streamflow simulations were generated using spatially lumped and distributed hydrologic models with and without the BBH Ptype data from 1 October 2005 through 30 September 2006.  ...  In this method, a fixed threshold temperature separating rain and snow was applied to hourly 4-km gridded temperature data.  ...  RFC, and Dan Gottas and one anonymous reviewer at the Earth System Research Laboratory for critical reviews that helped improve initial versions of this manuscript.  ... 
doi:10.1175/jhm-d-12-035.1 fatcat:e2ispqkw5fe7fhs6u7vtx536dm

Informing a hydrological model of the Ogooué with multi-mission remote sensing data

Cecile M. M. Kittel, Karina Nielsen, Christian Tøttrup, Peter Bauer-Gottwein
2018 Hydrology and Earth System Sciences  
We used a rainfall–runoff model based on the Budyko framework coupled with a Muskingum routing approach.  ...  In this study, we applied a multi-mission approach to force, calibrate and validate a hydrological model of the ungauged Ogooué river basin in Africa with publicly available and free remote sensing observations  ...  Daily historical in situ observations of discharge in the river basin recorded by the Office de la Recherche Scientifique et Technique Outre-Mer (ORSTOM) and the Direction Générale des Ressources Hydrauliques  ... 
doi:10.5194/hess-22-1453-2018 fatcat:g65xqa6yuvakppcx46j4ubgntm

Informing a hydrological model of the Ogooué with multi-mission remote sensing data

Cecile M. M. Kittel, Karina Nielsen, Christian Tøttrup, Peter Bauer-Gottwein
2017 Hydrology and Earth System Sciences Discussions  
We used a rainfall–runoff model based on the Budyko framework coupled with a Muskingum routing approach.  ...  In this study, we applied a multi-mission approach to force, calibrate and validate a hydrological model of the ungauged Ogooué river basin in Africa with publicly available and free remote sensing observations  ...  The performance measure is based on a scaled score approach.  ... 
doi:10.5194/hess-2017-549 fatcat:p7zdbwfhsree3p3v32vg37qmva

Climate Change and Tropical Andean Glacier Recession: Evaluating Hydrologic Changes and Livelihood Vulnerability in the Cordillera Blanca, Peru

Bryan G. Mark, Jeffrey Bury, Jeffrey M. McKenzie, Adam French, Michel Baraer
2010 Annals of the Association of American Geographers  
as a proxy for sequential changes in time.  ...  Much of this work makes a case for the transdisciplinary collaborations, place-based analyses, and mixed methodological approaches that we are currently utilizing in our research.  ... 
doi:10.1080/00045608.2010.497369 fatcat:vmwqljyw7faazpaewdor6btvly

A Model-Free Time Series Segmentation Approach for Land Cover Change Detection

Ashish Garg, Lydia Manikonda, Shashank Kumar, Vikrant Krishna, Shyam Boriah, Michael S. Steinbach, Durga Toshnival, Vipin Kumar, Christopher Potter, Steven A. Klooster
2011 Conference on Intelligent Data Understanding  
CIDU is unique in creating a forum for the applications of data mining and machine learning to earth sciences, space sciences, and aerospace and engineering systems.  ...  Selected papers will be invited for consideration for the CIDU special issue in the Statistical Analysis and Data Mining Journal.  ...  We would like to thank Jaya Kawale for help with the TC count data and Ryan Haasken for generating figures for the poster.  ... 
dblp:conf/cidu/GargMKKBSTKPK11 fatcat:yur5kmzxovbuxczf6lrix43viy

Using the minimum description length to discover the intrinsic cardinality and dimensionality of time series

Bing Hu, Thanawin Rakthanmanon, Yuan Hao, Scott Evans, Stefano Lonardi, Eamonn Keogh
2014 Data mining and knowledge discovery  
Choosing the best representation and abstraction level for a given task/dataset is arguably the most critical step in time series data mining.  ...  In this work, we investigate the problem of discovering the natural intrinsic representation model, dimensionality and alphabet cardinality of a time series.  ...  We would like to thank the reviewers for their helpful comments, which have greatly improved this work.  ... 
doi:10.1007/s10618-014-0345-2 fatcat:jywuttx6s5fhxlu5nlaamczqc4
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