x is the value that fundamentally drives the process model, but that we cannot observer perfectly. y is the value we observe that provides imperfect information about x. So, in this case, x is the number of flu cases, and y is the imperfect estimate of the number of flu cases provided by the search results for flu. Another common example would be when modeling changes in the number of individuals of some specie's population. We can go outside and count of the number of robins at a location repeatedly through time, but we won't see all of the robins that are there because some will be hidden in nests or blocked by trees. In this example x is the true number of robins, which is what influences that population dynamics, and y is the number of robins that we see, our imperfect estimate of x. Does that help?
Sorry about that. I'm not sure why the automated subtitles weren't working on this one (they should have been). I just posted a full hand edited set of subtitles and everything seems to be working now.
Hi David - Sorry about that. It looks like maybe they recently took the data down which is sad. It's still available in the Wayback Machine though at this link: web.archive.org/web/20200526050901/www.google.org/flutrends/about/data/flu/us/data.txt
I've added some additional information on getting and working with the data to the description. Because of the way the Wayback Machine works you'll need to go to the link provided and copy and paste the data from the web page into a text file and load it locally. Hope that helps and sorry for the inconvenience.
Last update - there's now a copy of this data available with a link that can be substituted directly into the material here: raw.githubusercontent.com/EcoForecast/EF_Activities/master/data/gflu_data.txt
Fantastic presentation! Thank you! Loved the outtakes 🤣
Glad you enjoyed it!
criminally underrated channel
wooow!!! You make State Space Modeling easy!!!!! thank u so much! you are incredibly clear
So glad it's useful!
Excellent explanation. I am going to subscribe
Thanks! Glad it was helpful.
Thanks for the explanation, how can I make a correlation between two raster stacks, representing two variables from 1988 to 2018?
Thank's very much
What's the difference between x and y in the example? I didn't quite get that
x is the value that fundamentally drives the process model, but that we cannot observer perfectly. y is the value we observe that provides imperfect information about x. So, in this case, x is the number of flu cases, and y is the imperfect estimate of the number of flu cases provided by the search results for flu.
Another common example would be when modeling changes in the number of individuals of some specie's population. We can go outside and count of the number of robins at a location repeatedly through time, but we won't see all of the robins that are there because some will be hidden in nests or blocked by trees. In this example x is the true number of robins, which is what influences that population dynamics, and y is the number of robins that we see, our imperfect estimate of x.
Does that help?
could you please open the live subtitle? kind of hard to catch up for non-native English speaker
Sorry about that. I'm not sure why the automated subtitles weren't working on this one (they should have been). I just posted a full hand edited set of subtitles and everything seems to be working now.
@@weecology I so appreciate your reply. It works! Your videos are very helpful!
@@yangwang6490 Thanks for letting us know about the issue and very glad to hear the videos are helpful!
Help. Data broken link =/
Hi David - Sorry about that. It looks like maybe they recently took the data down which is sad. It's still available in the Wayback Machine though at this link: web.archive.org/web/20200526050901/www.google.org/flutrends/about/data/flu/us/data.txt
I've added some additional information on getting and working with the data to the description. Because of the way the Wayback Machine works you'll need to go to the link provided and copy and paste the data from the web page into a text file and load it locally. Hope that helps and sorry for the inconvenience.
Last update - there's now a copy of this data available with a link that can be substituted directly into the material here: raw.githubusercontent.com/EcoForecast/EF_Activities/master/data/gflu_data.txt