Tweets by @bendfulcher about time-series analysis.
Sydney, New South Wales
Joined July 2015
- Tweets492
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What kinds of dynamical structures have scientists developed models for?
Introducing the 1000×1000 collection: 1000 simulated time series × 1000 samples from 133 dynamical processes, from noise and chaos to hearts, brains and climate.
Have a play? 🐛
dynamicsandneuralsystems.git…
Time Series Features retweeted
What kinds of dynamical structures have scientists developed models for?
Introducing the 1000×1000 collection: 1000 simulated time series × 1000 samples from 133 dynamical processes, from noise and chaos to hearts, brains and climate.
Have a play? 🐛
dynamicsandneuralsystems.git…
Time Series Features retweeted
Rivers, pendulums and the thalamus: how the brain balances stable and flexible dynamics, and what changes when consciousness fades.
Our latest journey into how the thalamus is shaping cortical dynamics in @ScienceAdvances 🧵1/10
science.org/doi/10.1126/scia…
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: github.com/DynamicsAndNeural…
Docs: dynamicsandneuralsystems.git…
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/ar…
#timeseries #dimensionreduction #complexsystems
@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
And there's an open python repo with really clear and easy to read and code implementing the key methods:
github.com/KieranOwens/tsdr
Take a look?! 👀
Time Series Features retweeted
New preprint!: "Using matrix-product states for time-series machine learning".
arxiv.org/abs/2412.15826
Quick summary below 👇
Time Series Features retweeted
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/brendanjohnharris…
Check out the CatchaMouse16 paper below
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/10.1101/…
Code: github.com/DynamicsAndNeural…
Short summary 👇
Time Series Features retweeted
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
Time Series Features retweeted
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/PhysRevX.14.…
Code details: time-series-features.gitbook…
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
Time Series Features retweeted
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 👇
Analyzing the Brain's Dynamic Response to Targeted Stimulation using Generative Modeling. arxiv.org/abs/2407.19737
Time Series Features retweeted
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/10.1101/…
Code: github.com/DynamicsAndNeural…
Short summary 👇
Time Series Features retweeted
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/2407.08987v1
Quick summary 👇
Time Series Features retweeted
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!
#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 😊
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…
"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/10.1101/2024.05.09.5…
Time Series Features retweeted
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/10.1101/2024.03.18.5…!