Basic Data Analysis for Time Series with R

by Dewayne R. Derryberry

Presents modern methods to analyzing data with multiple applications in a variety of scientific fields Written at a readily accessible level, Basic Data Analysis for Time Series with R emphasizes the mathematical importance of collaborative analysis of data used to collect increments of time or space. Balancing a theoretical and practical approach to analyzing data within the context of serial correlation, the book presents a coherent and systematic regression-based approach to model selection. The book illustrates these principles of model selection and model building through the use of information criteria, cross validation, hypothesis tests, and confidence intervals. Focusing on frequency- and time-domain and trigonometric regression as the primary themes, the book also includes modern topical coverage on Fourier series and Akaike's Information Criterion (AIC). In addition, Basic Data Analysis for Time Series with R also

320 pages, Hardcover

First published January 1, 2014

Reviews

[Tomek · Rating 1 out of 5]

While it may well be one of the few books focused on this particular topic, I found the mathematics unclear, which in turn obfuscated the methods/applications. The author uses the phrase, "it is obvious/self-evident/well-known" a bit too often, and then never really qualifies how or why particular actions are taken. It may be a good supplement to a course, which is what I believe the author intended, but it failed for me as a standalone resource for teaching yourself on this topic.

[Greenmantis · Rating 1 out of 5]

Not "Basic" at all.

The author wrote the book to himself. I wonder what audience the author is addressing. Not a student or anyone who has never seen the subject and wants a "basic" introduction.

As a self-study book, it is unusable. Unless you already now the material, then you do not need it.

There are better options of time series analysis books using R. Cryer & Sik-Chan, Cowpertwait, Shumway & Stoffer, to name a few.