Posted by **AlenMiler** at Jan. 24, 2017

English | 11 Dec. 2016 | ISBN: 1540809080 | 212 Pages | PDF (True) | 1.58 MB

Master Deep Time Series Forecasting with Python! Deep Time Series Forecasting with Python takes you on a gentle, fun and unhurried practical journey to creating deep neural network models for time series forecasting with Python.

Posted by **hill0** at Sept. 21, 2017

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 of impulse responses and forecasting in the stationary and non-stationary context, considering small sample correction,

Posted by **DZ123** at Sept. 10, 2017

English | 2000 | ISBN: 1584880635 | PDF | pages: 265 | 1.7 mb

Posted by **hill0** at Aug. 20, 2017

English | 27 May 2017 | ISBN: 0230243304 | 518 Pages | PDF | 5.3 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 of impulse responses and forecasting in the stationary and non-stationary context, considering small sample correction,

Posted by **nebulae** at Aug. 6, 2017

English | ISBN: 143986683X | 2014 | 469 pages | PDF | 15 MB

Posted by **nebulae** at Aug. 6, 2017

English | 2014 | ISBN: 1118808568 | 496 pages | PDF | 7 MB

Posted by **hill0** at Aug. 6, 2017

English | 20 Apr. 2017 | ISBN: 3319432516 | 642 Pages | PDF | 14.34 MB

This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications.

Posted by **naag** at July 31, 2017

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 2.5 Hours | 623 MB

Posted by **AvaxGenius** at July 29, 2017

The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty.

Posted by **hill0** at July 8, 2017

English | 20 Apr. 2017 | ISBN: 3319432516 | 642 Pages | PDF | 14.34 MB

This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a “theorem-proof” format,