Talk:Finding relations between variables in time series

Categorization

Currently this page is sorted in as a tool, though it's rather a meta-article. I wonder whether it would make more sense to file it under Topic instead but would love to hear second opinions on this! - Gedankenstuecke (talk) 12:53, 30 November 2021 (UTC)

if you think thats better then do it DG (talk)

I've moved it to a topic page and also restructured/renamed the page slightly to fit into the topic dimension. - Gedankenstuecke (talk) 08:56, 2 December 2021 (UTC)

todo suggestion

mp April 7th at 1:51 PM @ oh an additional thought, post chat — I think a “fast up / slow down” pattern would be reflected in a Markov probability distribution where any given number has a low probability of later numbers being higher than it. e.g. if prior was 7, then 7-or-lower are likely (gradual decline), but 8+ very unlikely.I was struggling to remember the language here, but I think a “first order” Markov model is where probability distributions at any given step are based only according to the previous step (and no further “memory” in the system). A “second order” is influenced by the two previous steps (a bit more memory).

First! Carefully compare variance internally to days and between days. If internal is too high this variable has too poor "distinctness". Can also look for "stability" in derivatives and between any time measured such as 12 hours or a month... or maybe strongly unlikely Markov model? Also maybe first, multimodality and outlier and anomalies.

potential sources of solutions

https://andrewncarr.gumroad.com/l/everydaydata

https://stats.stackexchange.com/questions/tagged/time-series wow really so much I forgot! Anomalies and Events and Periodicity! mentions QS : https://stats.stackexchange.com/questions/17623/how-to-detect-a-significant-change-in-time-series-data-due-to-a-policy-change/17661

https://hermandevries.nl/2020/09/23/relationships-between-hrv-sleep-and-physical-activity-in-personal-data/ suggested by gedankenstuek

http://beautifuldata.net/2015/01/how-to-analyze-smartphone-sensor-data-with-r-and-the-breakoutdetection-package/

http://beautifuldata.net/2015/01/how-to-analyze-smartphone-sensor-data-with-r-and-the-breakoutdetection-package/#comment-37605 for raw sensor data.

https://www.nature.com/articles/s41398-021-01445-0 personalized time series machine learning it is. Fairly commonly recommended procedures to Data scientists. I suspect faults from not takeing into account issues with time series; no mention of unit root for example. "Analytics code is available upon request from the corresponding author."

https://forum.quantifiedself.com/t/interventions-to-improve-sleep/9599/15 just linear lasso but lag and other issues discussed.

https://github.com/fasiha/ebisu#the-math intense math for flashcard prediction and timing adjustment!

https://old.reddit.com/r/CausalInference/ https://www.reddit.com/r/CausalInference/comments/ti18wz/personalized_nof1_or_singlecasesubject_causal/

https://www.physiq.com/ "physIQ is the only company that uses FDA-cleared, AI-based analytics to “learn” and detect even the most subtle changes in an individual’s own unique physiology 24/7."

https://play.google.com/store/apps/details?id=edu.brown.selfe&hl=en_CA&referrer=utm_source%3Dgoogle%26utm_medium%3Dorganic%26utm_term%3D%22self-e%22+app formal elf experiment support app from brown. definitely not advanced analytics but still

do not forget https://en.wikipedia.org/wiki/Multimodal_distribution is also a source

https://correlaid.org/en/ where to headhunt data scientists

other potential source

https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s897-machine-learning-for-healthcare-spring-2019/lecture-notes/MIT6_S897S19_lec14.pdf

https://www.microsoft.com/en-us/research/group/alice/ just let microsoft do it. https://econml.azurewebsites.net/spec/motivation.html

https://github.com/cuge1995/awesome-time-series

https://github.com/youngdou/awesome-time-series-analysis

https://github.com/ejain/n-of-1-ml

https://github.com/yzhao062/anomaly-detection-resources#32-time-series-outlier-detection

https://github.com/gianlucatruda/quantified-sleep analysis before designing an intervention

https://www.gwern.net/Causality

https://papers.nips.cc/paper/2019/file/42a6845a557bef704ad8ac9cb4461d43-Paper.pdf

https://ml4qs.org/ hoogendoorn and funke

to test algorithm generate data https://old.reddit.com/r/rstats/comments/nhenrm/recommend_r_packages_to_generate_data/ but is it time series? https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume13/cheng00a-html/node15.html

https://academic.oup.com/jamia/article/24/1/198/2631444

https://physionet.org/about/tutorial/#exploreanalyse

extra... https://forum.quantifiedself.com/search?q=matlab https://www.google.com/search?q=non+independence+of+observations https://www.google.com/search?q=time+series+distributions https://www.google.com/search?q=time+series+kernel+binning

maybe ask here again https://old.reddit.com/r/AskStatistics/ maybe datasets https://old.reddit.com/r/datasets/search?q=time+health+subreddit%3Adatasets&include_over_18=on&sort=relevance&t=all

https://cran.r-project.org/web/views/TimeSeries.html

https://www.nature.com/articles/s41598-017-05778-z

https://r-graph-gallery.com/266-ggplot2-boxplot-with-variable-width.html

https://www.jmir.org/2022/1/e28953

https://preprints.jmir.org/preprint/40238

https://www.jmir.org/2022/1/e30720

http://www.biostathandbook.com/independence.html