Logo

An Introductory Study on Time Series Modeling and Forecasting

Small book cover: An Introductory Study on Time Series Modeling and Forecasting

An Introductory Study on Time Series Modeling and Forecasting
by

Publisher: arXiv
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.

Home page url

Download or read it online for free here:
Download link
(880KB, PDF)

Similar books

Book cover: Introduction to Machine LearningIntroduction to Machine Learning
by - Cambridge University Press
Over the past two decades Machine Learning has become one of the mainstays of information technology and a rather central part of our life. Smart data analysis will become even more pervasive as a necessary ingredient for technological progress.
(4430 views)
Book cover: An Introduction to Statistical LearningAn Introduction to Statistical Learning
by - Springer
This book provides an introduction to statistical learning methods. It contains a number of R labs with detailed explanations on how to implement the various methods in real life settings and it is a valuable resource for a practicing data scientist.
(5335 views)
Book cover: A Brief Introduction to Machine Learning for EngineersA Brief Introduction to Machine Learning for Engineers
by - arXiv.org
This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment.
(1688 views)
Book cover: The Hundred-Page Machine Learning BookThe Hundred-Page Machine Learning Book
by
This is the first successful attempt to write an easy to read book on machine learning that isn't afraid of using math. It's also the first attempt to squeeze a wide range of machine learning topics in a systematic way and without loss in quality.
(1361 views)