Time Series Features

Tweets by @bendfulcher about time-series analysis.

[Time Series Features @compTimeSeries]

Highly comparative time-series analysis feature-extraction software (-hctsa-) now available in python as pyhctsa!

Compute *over 4500* time-series features from time-series data!

Code: https://t.co/Li88AImtpd

Docs: https://t.co/nOBhVm48g6

[Time Series Features @compTimeSeries]

And there's an open python repo with really clear and easy to read and code implementing the key methods:

https://t.co/njU2129n7f

Take a look?! 👀

[Time Series Features @compTimeSeries]

New preprint! (by @kieran_s_owens)

Of interest to anyone who analyzes time-series data!:

"Time-series dimension reduction: a comprehensive review and conceptual unification of algorithms"

https://t.co/mdoiHLBjPC

#timeseries #dimensionreduction #complexsystems

[Time Series Features @compTimeSeries]

@kieran_s_owens (i) explains how all the methods can be understood through these conceptual groupings,
(ii) derives new relationships between existing methods, and
(iii) provides some case-study demonstrations/comparisons of how (insanely) well they can work on data

[Ben Fulcher @bendfulcher]

New preprint!: "Using matrix-product states for time-series machine learning".
https://t.co/aZ6WMdICAc
Quick summary below 👇

[Brendan Harris @brendanjohnh]

Better believe it, there are now TWO #timeseries feature sets available in #julialang.

The new CatchaMouse16.jl package joins Catch22.jl, bringing 16 more features tailored to (mouse) fMRI data: https://t.co/wAEY4ORspP

Check out the CatchaMouse16 paper below

[Physical Review X @PhysRevX]

A new method of detecting criticality from time-series data outperforms conventional metrics in the presence of variable noise levels for both simulated systems and real neural recordings.

Read https://t.co/E8DVA3w19i

#PRXjustpublished #PRXopenaccess #PRXComplexSystems

[Ben Fulcher @bendfulcher]

Our work by @brendanjohnh (w Leo Gollo) on tracking the distance to criticality in noisy systems is now out in @PhysRevX 🙂 (includes an application tracking criticality across the mouse visual hierarchy)

https://t.co/dDKA1h3WWE

Code details: https://t.co/Yl8smK3614

[Ben Fulcher @bendfulcher]

New preprint by Rishi Maran @eli_j_muller

"Analyzing the Brain's Dynamic Response to Targeted Stimulation using Generative Modeling"

A review/perspective on why new mechanisms may be found by modeling brain stimulation dynamics 🧠⚡️

https://t.co/XTbti5wm1X

Quick summary 👇

[Ben Fulcher @bendfulcher]

New preprint w/ Imran Alam, Patrick Cahill @Valerio_Zerbi @m_markicevic @brendanjohnh @olivercliff

"Canonical time-series features for characterizing biologically informative dynamical patterns in fMRI"

https://t.co/eEGK6ZGKPv

Code: https://t.co/DgSPHqKGqM

Short summary 👇

[Ben Fulcher @bendfulcher]

Latest preprint: "Parameter Inference from a Non-stationary Unknown Process" (PINUP)

We're really interested in the problem of inferring sources of non-stationary variation directly from measured time-series data.

https://t.co/7KI02eTFpO

Quick summary 👇

[Ben Fulcher @bendfulcher]

If you're at OHBM this year, check out @AnnieGBryant's great work developing a systematic method to extract interpretable dynamical patterns from fMRI time series!

[Time Series Features @compTimeSeries]

@ZahraAlzneuro Nice! FYI the corresponding page for publications using catch22 is here:
https://t.co/jCy3KAWn7K

[Time Series Features @compTimeSeries]

Curious about scientific papers that have used hctsa for time-series feature extraction?
I maintain a log of this here, categorized across Biology, Cellular Neuroscience, Neuroimaging, Medicine, Pathology, Engineering, Geoscience:
https://t.co/oAFboRdGKH

[Time Series Features @compTimeSeries]

@DrScienceMan1 Features in hctsa as coded assume a uniformly sampled series in time. Would need to adapt methods to non uniformly sampled data, or to use hctsa as it currently is, interpolate data. Although hctsa in general better suited to higher temporal resolution and lower noise modalities

[Time Series Features @compTimeSeries]

"Extensive MEG time-series phenotyping unveils neural markers predictive of age"
Using the hctsa time-series feature set, finding age-predictive patterns of autocorrelation within the visual and temporal cortex.
https://t.co/RC4cv0iedW

[Lukas Roell @RoellLukas]

@misicbata @olivercliff @bendfulcher @compTimeSeries Amazing work using the great pyspi package! We also used it in our recent work and examined the sensitivity of (only) 20 representative FC metrics regarding neural decline induced by age and malignant brain tumors https://t.co/2ou0NNHYkz!

[Bratislav Misic @misicbata]

Benchmarking methods for mapping functional connectivity in the brain | https://t.co/C9kCKmmNOY

What is the best FC metric?

Led by @liuzhenqi0303 avec @loopyluppi @JustineYHansen @yetianmed @AndrewZalesky @bttyeo @bendfulcher ⤵️

[Time Series Features @compTimeSeries]

catch22 documentation for efficient time-series feature extraction is now live on @GitBookIO, with docs for #RStats #Python #Julia and #Matlab and full descriptions of all time-series features
https://t.co/ckKSjrymRD