PPT-UCR Time Series Classification Archive

Please reference as Yanping Chen Eamonn Keogh Bing Hu Nurjahan Begum Anthony Bagnall Abdullah Mueen and Gustavo Batista 2015 The UCR Time Series Classification Archive URL wwwcsucredueamonntimeseriesdata. Basic time series. Data on the outcome of a variable or variables in different time periods are known as time-series data.. Time-series data are prevalent in finance and can be particularly challenging because. John . Halquist. Development and Operations Officer. National Operational Hydrologic Remote Sensing Center (NOHRSC). HIC Meeting – Feb 2010. Major categories for Archive use. General Archive requirements. Time Series in High Energy Astrophysics. Brandon C. Kelly. Harvard-Smithsonian Center for Astrophysics. Lightcurve. shape determined by time and parameters. Examples: . SNe. , . γ. -ray bursts. Can use . Authors. Jessica Lin. Eamonn. Keogh. Li Wei. Stefano . Lonardi. Presenter. Arif. Bin . Hossain. Slides incorporate materials kindly provided by Prof. . Eamonn. Keogh. Time Series.  A . time series. Some Basic Concepts. Reference : Gujarati, Chapters 21. Course . Incharge. : . Prof. Dr. . Himayatullah. Khan. Time Series Data. One of the . important. and . frequent. types of data used in empirical . Africa Regional Workshop on the Building of Sustainable National. Greenhouse Gas Inventory Management Systems, and the Use of the 2006. IPCC Guidelines for National Greenhouse Gas Inventories. Swakopmund. Bing . Hu . Yanping. Chen . Eamonn. Keogh. SIAM Data Mining Conference (. SDM. ), 2013. Outline. Motivation. . Proposed Framework. . . - Concepts. . - Algorithms. . Experimental Evaluation. Some Basic Concepts. Reference : Gujarati, Chapters 21. Course . Incharge. : . Prof. Dr. . Himayatullah. Khan. Time Series Data. One of the . important. and . frequent. types of data used in empirical . Abdullah Mueen 5 Slides Demo Primitives for Time Series Data Mining Time series motifs Time series shapelets Time series join 0 2000 4000 6000 8000 10000 0 10 20 30 0 100 200 300 400 500 600 700 800 with recurrent neural networks. Aymen. . Cherif. . , Hubert . Cardot. , . Romuald. Bone. 2011, . Necurocomputing. Presented by . Chien-Hao. Kung. 2011/11/3. 2. Outlines. Motivation. Objectives. Methodology. SRA Sequence Read Archive Collection of sequence data from next-generation sequencing technology for different organisms https//wwwncbinlmnihgov/sra/ https//wwwncbinlmnihgov/Traces/sra/ National Cen W. Art Chaovalitwongse. Rutgers University. *Joint work with Y.J. Fan (Rutgers) and R.C. Sachdeo (Jersey Shore University Hospital). This work is supported in part by research grants from . NSF CAREER Grant CCF . Chapter 18. Learning Objectives. LO18-1. Define and describe the components of a time series.. LO18-2. Smooth a time series by computing a moving average.. LO18-3. Smooth a time series by computing a weighted moving average.. Book: Time Series Analysis Univariate and Multivariate. http://ruangbacafmipa.staff.ub.ac.id/files/2012/02/Time-Series-Analysis-by.-. Wei.pdf. https://wiki.math.ntnu.no/tma4285/2011h/start. http://astro.temple.edu/~wwei/data.html.