Rob J Hyndman, a statistics professor, writes about forecasting, cross-validation, data transformation, and good graphics.
Commenters recommend his blog and book, and cite his papers on forecast accuracy (MASE) and his review of Google Scholar metrics.
From my experience, these resources are worth read:
[1] Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop
Andreas Brandmaier's permutation distribution clustering is a method rooted in the dissimilarities between time series, formalized as the divergence between their permutation distributions. Personally, I think this is your "best" option
http://cran.r-project.org/web/packages/pdc/index.html
Eamonn Keogh's SAX (Symbolic Aggregate Approximation) and iSAX routines develop "shape clustering" for time series
http://www.cs.ucr.edu/~eamonn/SAX.htm
There are approaches based on text compression algorithms that remove the redundancy in a sequence of characters (or numbers), creating a kind of distance or density metric that can be used as inputs to clustering, see, e.g.:
http://link.springer.com/chapter/10.1007/978-0-387-84816-7_4
This paper by Rob Hyndman Dimension Reduction for Clustering Time Series Using Global Characteristics, discusses compressing a time series down to a small set of global moments or metrics and clustering on those:
http://www.robjhyndman.com/papers/wang2.pdf
Chapter 15 in Aggarwal and Reddy's excellent book, Data Clustering, is devoted to a wide range (a laundry list, really) of time-series clustering methods (pps 357-380). The discussion provides excellent background to many of the issues specific to clustering a time…
Where are the statistics blogs?
Christian Robert https://xianblog.wordpress.com/
Andrew Gelman https://statmodeling.stat.columbia.edu/
Deborah Mayo https://errorstatistics.com/
Frank Harrell https://www.fharrell.com/
Radford Neal https://radfordneal.wordpress.com/
Variance Explained http://varianceexplained.org/
Larry Wasserman (inactive) https://normaldeviate.wordpress.com/
20% Statistician https://daniellakens.blogspot.com/
Jim Albert https://baseballwithr.wordpress.com/author/bayesball/
The R blog https://developer.r-project.org/Blog/public/
Graphic Detail, by The Economist https://www.economist.com/graphic-detail/rss.xml
The Royal Society Data Science Section https://rssdss.design.blog/blog-feed/
Many good “blogs” can be created by following good users on Cross Validated. StackExchange provides RSS feeds of user activity.
Many statistics journals also offer RSS feeds. These feeds are sometimes hidden, but an RSS reader like NewsBlur can discover them for you. This post[1] is a good starter collection of statistics journal RSS feeds.
[1] https://robjhyndman.com/hyndsight/rss-feeds/
I think what John is saying is that people commonly use R^2 as measure of model fit where something like root mean squared error (RMSE) gives a better measure of model fit (by measuring the distance from the true model) depending on the model. Just using R^2 blindly for most tasks you would work on can lead to choosing an incorrect model.
I think the main take-away from the post is to better understand the correct measure for model fit for your specific data. For example, if you are forecasting a time series with stationary demand, mean average deviation might be the best measure of model fit, but it the case where there is seasonality with a trend, the RMSE would be a better measure [1].
[1] http://robjhyndman.com/papers/foresight.pdf
Rob J Hyndman has a very nice review on Google scholar metrics [1]. Here is his ending quote:
In summary, the h5-index is simple to understand, hard to
manipulate, and provides a reasonable if crude measure of
the respect accorded to a journal by scholars within its
field.
While journal metrics are no guarantee of the quality of a
journal, if they are going to be used we should use the
best available, and Google’s h5-index is a big improvement
on the ISI impact factor.
[1] http://robjhyndman.com/researchtips/google-scholar-metrics/
I believe as long as the transformation function is invertible (no information is lost), it ought to be a valid approach. The Box-Cox function as far as I can tell is invertible.
If a transformation f has the property (f^-1 o f)(x) = x then (f^-1 o g o f)(x) should be valid -- though how to rigorously demonstrate this in all cases evades me for now. (there are some conditions that need to be satisfied)
Rob Hyndman, the author of the "forecast" package in R, also thinks it's ok (slide 17 [1])
[1] https://robjhyndman.com/talks/RevolutionR/7-Transformations.pdf
Cross validation for time series, goes something like this:
http://robjhyndman.com/hyndsight/crossvalidation/
(near bottom of the article)
Anyways, as somebody who has spent GIANT amounts of time experimenting with machine learning using market data (mainly stocks), I can give you TONS algorithms I've created that would show similar, even much higher, gains when you only test on 6 weeks of data. Been there done that. For example 6 weeks you make a 100% return, then in the next 3 weeks you suffer a 50% loss. Reality sets in...