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After years of development, am excited to announce the launch of our new self-organizing drag-and-drop library for sharing and exploring diverse time-series data! Have a play! 🤓
comp-engine.org
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Time Series Features
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Time Series Features
@compTimeSeries
Tweets by @bendfulcher about time-series analysis.
Sydney, New South Wales Katılım Temmuz 2015
77 Takip Edilen764 Takipçiler
And there's an open python repo with really clear and easy to read and code implementing the key methods:
github.com/KieranOwens/ts…
Take a look?! 👀
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@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
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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"
techrxiv.org/users/999518/a…
#timeseries #dimensionreduction #complexsystems
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Time Series Features retweetledi
New preprint!: "Using matrix-product states for time-series machine learning".
arxiv.org/abs/2412.15826
Quick summary below 👇
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Time Series Features retweetledi
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: github.com/brendanjohnhar…
Check out the CatchaMouse16 paper below
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" biorxiv.org/content/10.110… Code: github.com/DynamicsAndNeu… Short summary 👇
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Time Series Features retweetledi
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 go.aps.org/3WBjcXf
#PRXjustpublished #PRXopenaccess #PRXComplexSystems
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Time Series Features retweetledi
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)
doi.org/10.1103/PhysRe…
Code details: time-series-features.gitbook.io/time-series-an…
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 go.aps.org/3WBjcXf #PRXjustpublished #PRXopenaccess #PRXComplexSystems
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Time Series Features retweetledi
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 🧠⚡️
arxiv.org/abs/2407.19737
Quick summary 👇
Quantitative Biology@BioPapers
Analyzing the Brain's Dynamic Response to Targeted Stimulation using Generative Modeling. arxiv.org/abs/2407.19737
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Time Series Features retweetledi
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"
biorxiv.org/content/10.110…
Code: github.com/DynamicsAndNeu…
Short summary 👇
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Time Series Features retweetledi
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.
arxiv.org/abs/2407.08987…
Quick summary 👇
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Time Series Features retweetledi
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!
Annie G. Bryant@AnnieGBryant
#OHBM very excited to share this (v2.0) at the 'Transdiagnostic Perspectives on Neurodevelopmental and Psychiatric Disorders - Part 1' 12pm oral session on Tuesday, and poster #1740 on Wed/Thurs afternoon! Come say hi 😊
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@ZahraAlzneuro Nice! FYI the corresponding page for publications using catch22 is here:
time-series-features.gitbook.io/catch22/welcom…
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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:
time-series-features.gitbook.io/hctsa-manual/i…
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@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
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@compTimeSeries Well this is awesome! How does hctsa handle fMRI data that has been been motion scrubbed due to in-scanner motion?
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"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.
doi.org/10.1101/2024.0…
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Time Series Features retweetledi
@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 doi.org/10.1101/2024.0…!
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Time Series Features retweetledi
Benchmarking methods for mapping functional connectivity in the brain | doi.org/10.1101/2024.0…
What is the best FC metric?
Led by @liuzhenqi0303 avec @loopyluppi @JustineYHansen @yetianmed @AndrewZalesky @bttyeo @bendfulcher ⤵️
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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
time-series-features.gitbook.io/catch22
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Time Series Features retweetledi
#Satellite ComplexTime explores temporal dynamics in complex systems across various domains. They invite submissions on topics related to temporal data handling, methods, and tools. Learn more at: sites.google.com/view/complexti…
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