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
This book concentrates on the important ideas in machine learning, to give the reader sufficient preparation to make the extensive literature on machine learning accessible. The author surveys the important topics in machine learning circa 1996.
(30765 views)
Book cover: Machine Learning, Neural and Statistical ClassificationMachine Learning, Neural and Statistical Classification
by - 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.
(29117 views)
Book cover: Foundations of Machine LearningFoundations of Machine Learning
by - The MIT Press
This is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools.
(6917 views)
Book cover: Introduction to Machine LearningIntroduction to Machine Learning
by - arXiv
Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).
(23146 views)