Posted by **hill0** at Feb. 16, 2018

English | 27 May 2017 | ISBN: 0230243304 | 518 Pages | EPUB | 3.9 MB

Posted by **DZ123** at Feb. 12, 2018

English | 2016 | ISBN: 1493965670 | PDF | pages: 298 | 6.0 mb

Posted by **Underaglassmoon** at Feb. 7, 2018

Oxford University | English | 2017 | ISBN-10: 0198782934 | 384 pages | PDF | 30.91 mb

by Ray Huffaker (Author), Marco Bittelli (Author), Rodolfo Rosa (Author)

Posted by **Underaglassmoon** at Jan. 25, 2018

Wiley | English | 2018 | ISBN-10: 1119096960 | 304 pages | PDF | 7.47 mb

by Christian H. Weiss (Author)

Posted by **hill0** at Jan. 24, 2018

English | 2018 | ISBN: 9789535137436 | 170 Pages | PDF | 16 MB

Posted by **hill0** at Jan. 22, 2018

English | 27 May 2017 | ISBN: 0230243304 | 518 Pages | EPUB | 3.9 MB

This book examines conventional time series in the context of stationary data prior to a discussion of cointegration, with a focus on multivariate models. The authors provide a detailed and extensive study

Posted by **Jeembo** at Jan. 21, 2018

English | 2017 | ISBN: 3319557882 | 414 Pages | PDF | 22.8 MB

This volume of selected and peer-reviewed contributions on the latest developments in time series analysis and forecasting updates the reader on topics such as analysis of irregularly sampled time series, multi-scale analysis of univariate and multivariate time series, linear and non-linear time series models, advanced time series forecasting methods, applications in time series analysis and forecasting, advanced methods and online learning in time series and high-dimensional and complex/big data time series.

Posted by **arundhati** at Jan. 17, 2018

2017 | ISBN-10:1788290224 | 244 pages | PDF | 12 MB

Posted by **roxul** at Jan. 15, 2018

English | 16 Jun. 2016 | ISBN: 3319287230 | 406 Pages | EPUB | 6 MB

Posted by **Jeembo** at Jan. 14, 2018

English | 2017 | ISBN: 9811064350 | 133 Pages | PDF | 6.4 MB

This book introduces academic researchers and professionals to the basic concepts and methods for characterizing interdependencies of multiple time series in the frequency domain.