PPT-1 SOM time series clustering and prediction

with recurrent neural networks Aymen Cherif Hubert Cardot Romuald Bone 2011 Necurocomputing Presented by ChienHao Kung 2011113 2 Outlines Motivation Objectives Methodology. † To whom correspondence should be addressed. Email: chan@postech.ac.kr 1. INTRODUCTION For iron and steel industries, it is very important to reduce energy costs due to their tremendous consum Clustering Time Series Streams. Requires Ignoring Some Data. Thanawin. . Rakthanmanon. Eamonn. Keogh. Stefano . Lonardi. Scott Evans. Subsequence Clustering Problem. Given a . time . series, individual . Yongin. Kwon, . Sangmin. Lee, . Hayoon. Yi, . Donghyun. Kwon, . Seungjun. Yang, . Byung. -. Gon. Chun,. Ling Huang, . Petros. . Maniatis. , . Mayur. . Naik. , . Yunheung. . Paek. USENIX ATC’13. 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. Nishant Pandey. Synopsis. Problem statement and motivation. Previous work and background. System. Intuition and Overview. Pre-processing of audio . signals. Building . f. eature space. Finding patterns in unlabelled data and labelling of samples. Discovering Objects with Predictable Context. Carl . Doersch. , . Abhinav. Gupta, Alexei . Efros. Unsupervised Object Discovery. Children learn to see without millions of labels. Is there a cue hidden in the data that we can use to learn better representations?. From Business Intelligence Book by . Vercellis. Lei Chen. , . for COMP 4332. 1. Definitions. Data: {. x_i. , . y_i. , . i. =1, 2…}. Discrete: . x_i. are discrete: day 1, day 2, …. Continuous. x_i. 1. Xiaoming Gao, Emilio Ferrara, Judy . Qiu. School of Informatics and Computing. Indiana University. Outline. Background and motivation. Sequential social media stream clustering algorithm. Parallel algorithm. Cynthia Sung, Dan Feldman, Daniela . Rus. October 8, 2012. Trajectory Clustering. 1. Background. Noise. Sampling frequency. Inaccurate control. SLAM . [. Ranganathan. and . Dellaert. , 2011; Cummins and Newman, 2009; . Data . Mining Algorithm. Peter Myers. Bitwise Solutions Pty Ltd. DBI-B326. Presenter Introduction. Peter Myers. BI Expert, Bitwise Solutions Pty Ltd. BBus. , SQL Server MCSE, MCT, SQL Server MVP (since 2007). Ge. . Song. *. +. ,. . Zide. . Meng. *. ,. . Fabrice. . Huet. *. ,. . Frederic. . Magoules. +. ,. . Lei. . Yu. #. . and. . Xuelian. . Lin. #. * . University. . of. . Nice. . Sophia. TRACDS. Middle East Technical University. October . 31, . 2012. Margaret . H . Dunham, . Michael . Hahsler, Yu . Su, . Sudheer. . Chelluboina. , . and Hadil Shaiba. Computer . Science and . Engineering . Saehoon Kim. §. , . Yuxiong He. *. ,. . Seung-won Hwang. §. , . Sameh Elnikety. *. , . Seungjin Choi. §. §. *. Web Search Engine . Requirement. 2. Queries. High quality + Low latency. This talk focuses on how to achieve low latency without compromising the quality. STAT 689. forecasting. Forecasting is the process of making predictions of the future based on past and present data!. forecasting. Coming up with predictions is important.. It is also very hard since none has the correct model of the world..