Do the running averages take into consideration all of the data points? Are you familiar with bounded convolutions or the reasoning behind using them? I will give you a hint: when you do a rolling average on every point, every data point is weighted equally.
Oh and by all means please point to the part of your blog that gives a basis for not using a bounded convolution like they do to filter noise.
Right off the bat the statement from the blog:
Is wrong. A low pass filter does not behave in this manner. A low pass filter excludes particular data points a running average does not. if your point is that BEST is using a low pass filter approach you are flat ass wrong.The various black lines are the actual data! The red-line is a 10-year running mean smoother! I will call the black data the real data, and I will call the smoothed data the fictional data. Mann used a “low pass filter” different than the running mean to produce his fictional data, but a smoother is a smoother and what I’m about to say changes not one whit depending on what smoother you use.
This statement is also patently false:
Avionic controls is one application that you use this for as they also take rolling averages off of sensors and feed them into the control system. That is just off the top of my head.Now I’m going to tell you the great truth of time series analysis. Ready? Unless the data is measured with error, you never, ever, for no reason, under no threat, SMOOTH the series! And if for some bizarre reason you do smooth it, you absolutely on pain of death do NOT use the smoothed series as input for other analyses!
There is no basis given for this statement
Can you explain how when dealing with noisy systems, using convolutions of this nature reduces accuracy? He does not.If, in a moment of insanity, you do smooth time series data and you do use it as input to other analyses, you dramatically increase the probability of fooling yourself! This is because smoothing induces spurious signals—signals that look real to other analytical methods. No matter what you will be too certain of your final results! Mann et al. first dramatically smoothed their series, then analyzed them separately. Regardless of whether their thesis is true—whether there really is a dramatic increase in temperature lately—it is guaranteed that they are now too certain of their conclusion.





Keep it up.


