Time Series Analysis, by James Sogi

April 23, 2009 |

Time Series Analysis with Applications in R, by Jonathan D. Cryer‏ and Kung-Sik Chan, was advertised to have the R code for its examples, but didn't. It has a few snippets for a few charts and an Introduction to R in the appendix. Kind of a rip off. I don't especially recommend it for those looking for some R code. There are the infuriating questions at the end of the chapter. I know you are supposed to work them out understand the material but I want a reference book. I'm not in college any more. I want the answers. It's more suited for entry-level college statistics.

There was an interesting chapter on trends in which they distinguish between stochastic and deterministic trends. Stochastic trends such as a random walk, have apparent "trends" merely as an artifact of the strong positive correlation between the series values at nearby time points and the increasing variance in the process as time goes by. This answers my question of why the time series charts line up. They distinguish deterministic trends, for example, the upward trend in temperature as summer approaches. There is a reason, a model for the trend, the tilt of the earth towards the sun causing higher temperatures.

I've become partial to The R Book by Michael Crawley. A solid intermediate text with a walkthrough of various practical stats concepts. Best in electronic format at 950 pages.

James Sogi  writes:

Quick study of Spus shows historical variance increases in the afternoons which is in line with Cryes theory of appearance of trends in random walk and autocorrelation of near time series points in stochastic trend and increase in variance. This ties together with the thread on apparent trends in spu series. Interesting how there's always a fresh way of looking at the same old stuff.

On a different subject, Soros says in his update to his recent book Reflections on the Crash of 2008 that it is wrong to model equities on the same basis as natural models like we often do here, like Lotka-Volterra etc, as human reflexivity and self perception leads to bigger trends, panics, booms, etc than natural phenomenon which is not self aware. He's not sure how to model reflexivity and is afraid of locking in a model to a fixed algo.

A counter example in nature would be study of stampedes, lemmings, migrations, panics, temperature spikes clusters, hurricanes and extreme events, earthquakes, and other outlier type natural behavior or other discontinuous or extreme type data. We'll have the 2008-9 data in our series going forward, so the model might adjust itself, and if not the model, the data will be there. Question is will means revert. For self protection we must err on side of different lower kurtosis plus fatter tails described more as Pearson Type Vii or Student t or Cauchy type distribution. Got a bit of negative skew in there now too.

-9 | 96
-8 |
-7 | 0
-6 | 833
-5 | 74433
-4 | 98753300
-3 | 98443210
-2 | 998877666553322200
-1 | 99877777766655555444432222222111111000
-0 | 97777765432111110
0 | 0011122222334444455556666666667777888999
1 | 0001112233336667889999
2 | 012233446677
3 | 01112233344447
4 | 011139
5 | 4468
6 |
7 |
8 |
9 |
10 | 4
11 |
12 | 6

Like the expert distinguished prof, the Palindrome experienced systemic breakdown as a young man in Hungary. This must be a life changing experience. For everyone who lived through 2008 it also will be a life changing experience, though not on the same scale as Budapest 1943 or in Lebanon, or the Balkans, Malay, China, Russia, Africa.

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