An Introductory Study on Time Series Modeling and Forecasting
by Ratnadip Adhikari, R. K. Agrawal
Publisher: arXiv 2013
Number of pages: 67
Description:
The aim of this dissertation work is to present a concise description of some popular time series forecasting models used in practice, with their salient features. In this thesis, we have described three important classes of time series models, viz. the stochastic, neural networks and SVM based models, together with their inherent forecasting strengths and weaknesses.
Download or read it online for free here:
Download link
(880KB, PDF)
Similar books

by Owain Evans, et al. - AgentModels.org
This book describes and implements models of rational agents for (PO)MDPs and Reinforcement Learning. One motivation is to create richer models of human planning, which capture human biases. The book assumes basic programming experience.
(4240 views)

by Jan-Willem van de Meent, et al. - arXiv.org
This text is designed to be a graduate-level introduction to probabilistic programming. It provides a thorough background for anyone wishing to use a probabilistic programming system, and introduces the techniques needed to build these systems.
(3384 views)

by Carl E. Rasmussen, Christopher K. I. Williams - The MIT Press
Gaussian processes provide a principled, practical, probabilistic approach to learning in kernel machines. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.
(25340 views)

by Gianluca Bontempi, Souhaib Ben Taieb
This handbook aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. This manuscript aims to find a good balance between theory and practice.
(7292 views)