
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
Machine Learning, Neural and Statistical Classificationby D. Michie, D. J. Spiegelhalter - Ellis Horwood
The book provides a review of different approaches to classification, compares their performance on challenging data-sets, and draws conclusions on their applicability to realistic industrial problems. A wide variety of approaches has been taken.
(30655 views)
An Introduction to Probabilistic Programmingby 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.
(6261 views)
Algorithms for Reinforcement Learningby Csaba Szepesvari - Morgan and Claypool Publishers
We focus on those algorithms of reinforcement learning that build on the theory of dynamic programming. We give a comprehensive catalog of learning problems, describe the core ideas, followed by the discussion of their properties and limitations.
(10017 views)
Optimal and Learning Control for Autonomous Robotsby Jonas Buchli, et al. - arXiv.org
The starting point is the formulation of of an optimal control problem and deriving the different types of solutions and algorithms from there. These lecture notes aim at supporting this unified view with a unified notation wherever possible.
(7239 views)