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review 2017-06-08 09:12
Time Series Analysis and its Applications, Shumway and Stoffer
Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics) - David S. Stoffer,Robert H. Shumway

I read the first ~100p of this book. I stopped because the subject matter had diverged too far from my area of immediate interest (which was covered in the first chapter) rather than because the book is bad. In fact I think it is a good introduction to the topic for those with an interest and a background covering "normal" statistics to a level most STEM undergrads would have. Perhaps one thing that became obvious to me by inference should have been made explicit at the outset, which is that the fundamental general approach is as follows:

 

1. Get time series and plot it.
2. Guess any trends and/or periodicities in the data (various methods)
3. Subtract them (various methods)
4. Examine what's left ("residuals") to see if it behaves like noise (i.e. has some known type of random distribution) (various methods)
5. If it does, YAY! You have a usable model of the time series
6. If it does not, either make further guesses about trends/periodicities in the residuals and repeat from step 2 OR
7. Go back to the original time series and start from step 2 with different guesses about the nature of trends/periodicities

 

A flow chart of this at the beginning of the book would make what the book is actually about crystal clear.

 

As mentioned in a status update, the book does not assume the reader is scientifically motivated and does not discuss the meaning or validity of any trends, correlations or periodicities discovered. There are applications where this is entirely legitimate, probably the biggest and most utilised being analysis of financial/economic data for purposes of investment or trading: One only needs a model that works and not an explanation of why it works in order to make practical decisions. I would advise budding scientists to approach with caution, however; this form of analysis can only generate empirical models and hypotheses about why they are true are a separate but essential part of the scientific process. So, for example, if one discovers a model of the form, seasonal oscillation + white noise, describing your time series, one can make predictions about the future but there is no explanation of why the seasonal variation occurs. You are only part way there, scientifically.

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text 2017-06-07 09:18
Reading progress update: I've read 90 out of 604 pages.
Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics) - David S. Stoffer,Robert H. Shumway

A thorough grounding in basic "normal" statistics is required. An interesting observation is that the book (and the field) is not necessarily scientifically motivated; the presence of trends/periodicities/correlations may be detected or modeled but there is no discussion of whether or how they are causal or what they mean.

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text 2017-06-05 08:55
Reading progress update: I've read 17 out of 604 pages.
Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics) - David S. Stoffer,Robert H. Shumway Extremely early days but so far the presentation is very clear.
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review 2017-03-13 18:37
Information Theory, James Stone
Information Theory: A Tutorial Introduct... Information Theory: A Tutorial Introduction - James V. Stone

Eggzellent stuff!

 

 

What a great intro to a subject I found fascinating and is widely applicable: Digital communications, computing, neuro-science and other biological sciences, linguistics (a favourite) and then there's my secret application that made me want to read the book in the first place...but you won't find it in the book. There is a proper glossary of technical terms, something that long term readers of my reviews know I think is essential and yet all too frequently absent. There are also appendices on various topics in probability and statistics that are relevant and you may be unfamiliar with or in need of a quick refresher about. This is also good textbook writing, in my view, as is including XKCD cartoons (with permission). The latter are even relevant!

 

I found it straightforward to follow what was going on despite having been solidly rebuffed by my previous encounters with the subject. I think this is mainly because some opaque terminology is properly and thoroughly defined and explained and put into a practical context as soon as possible. I strongly recommend this if you ever have a need to learn the basics of the subject and thanks to whomever recommended it to me!

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text 2017-03-13 08:49
Reading progress update: I've read 185 out of 260 pages.
Information Theory: A Tutorial Introduct... Information Theory: A Tutorial Introduction - James V. Stone

Stone says there is an energy limit (Landauer's Limit) below which acquiring information is impossible. This 0.693 Joules/bit. This apparently contradicts Feynman in his Lectures on Computation. The solution; Landauer's limit applies only to IRREVERSIBLE computations, where-as Feynman is talking about REVERSIBLE computing.

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