#programming #graphing #plotting #visualization #timeSeries #gnuplot #commonLisp #lisp #example https://screwlisp.small-web.org/programming/common-lisp-invoking-gnuplot/
I could not even find my own previous articles and #demos of this online!

I used #uiop run-program to handle one specific case like

(gnuplot "bad title" '((1 2) (3 4)) '((5 6) (7 8)))
or equivalently,
(apply 'gnuplot "bad title" '(((1 2) (3 4)) ((5 6) (7 8))))

Do you personally have an example? I remember it being hard to dredge up gnuplot examples but this is beyond silly.

I am starting an email newsletter about time series analysis and forecasting. It is still WIP, but you can subscribe here:
https://the-forecaster.beehiiv.com/subscribe

Inferring parameters of #microbiome dynamics: @romanzapien @FlorenceBansept & @EvolTheoArne use stats info from #TimeSeries replicates to infer microbial interaction parameters & their uncertainty, so improving predictions & model precision #PLOSBiology https://plos.io/3CU1RT1

Excellent blog post by Nick Clark showing how to work with state-space vector autoregressive models for non-Gaussian time-series, or in less technical terms - most ecological time-series. So if you model ecological time-series definitely go give it a read:

https://ecogambler.netlify.app/blog/vector-autoregressions/

and checkout the mvgam package:

https://nicholasjclark.github.io/mvgam/

You won't be surprised that I eagerly watch James Hoffmann's videos. Especially the "if you were to plan some small experiment on your own - WITH COFFEE!" videos are really good.

So his new test of the "delay your morning #coffee" hypothesis was right down my street!
https://www.youtube.com/watch?v=yCJr49GU9yY
#HubermanLab

One thing I was wondering and which was not discussed in the comments I managed to read:
Were the data analysed in a way that took nesting / #RepeatedMeasures into account?

Testing #significance of a correlation between time series is a longstanding challenge. @ShouGroup presents the truncated time-shift (TTS) test, a significance test that is valid as long as one #TimeSeries is statistically stationary #PLOSBiology https://plos.io/4dOvvGx

Long-Term Ocean Sampling In Narragansett Bay Reveals Plummeting Plankton Levels - Impact Uncertain For Local Food Web
--
https://phys.org/news/2024-05-term-ocean-sampling-narragansett-bay.html <-- shared technical article
--
https://doi.org/10.1073/pnas.2311086121 <-- shared paper
--
#GIS #spatial #mapping #sampling #phytoplankton #Narragansett #NarragansettBay #spatialanalysis #spatiotemporal #timeseries #bloom #foodchain #risk #hazard #ecosystems #climatechange #biomass #RO #RhodeIsland #coast #coastal #nutrients #temperature #salinity #global #ocean #marine #plankton #impacts #future

Introducing LongMemory.jl: A Julia Package for Long Memory Time Series Analysis

I am happy to announce that after several months of getting to understand the language better, I have finally published my first Julia registered package: LongMemory.jl. This package is the result of my research on long memory time series analysis, which is a fascinating topic in econometrics and statistics. Long memory models are useful for capturing the persistence and dependence of many real-world phenomena, such as inflation, interest rates, volatility, network traffic, and environmental data.

LongMemory.jl makes it easy to generate, estimate, and forecast long memory models in Julia. It supports various types of models, such as fractional differencing, cross-sectional aggregation, and stochastic duration shocks. It also provides functions for testing the presence of long memory, computing the Hurst exponent, and simulating long memory processes. The package is fully documented and includes classical data examples, such as the Nile River minima.

The package can be installed easily from the Julia general registry. I have prepared a short video that shows how to install the package and generate long memory diagnostics plots for the Nile River minima dataset. The Nile River minima is a famous example of a long memory time series.

I hope you find LongMemory.jl useful and practical. I welcome any feedback, suggestions, or contributions to improve the package. You can contact me or open an issue on GitHub. Thank you for your interest and feedback!

#julialang #programming #programmingjourney #longmemory #timeseriesanalysis #timeseries #econometrics #statistics @julialanguage@bird.makeup @julialanguage@mastodon.social

Using transformers for time series forecasting

I came across this interesting blog post on HuggingFace by Eli Simhayev, Kashif Rasul, and Niels Rogge, providing empirical comparison and benchmarking between Transformers and linear models for forecasting applications.

TLDR: transformers performed better than the linear models

Resources
Paper: https://huggingface.co/blog/autoformer
Colab: https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/autoformer-transformers-are-effective.ipynb

Super excited to share that only after 4 years of being #opensource, the #stumpy #python package for modern #timeseries analysis just eclipsed 5+ Million downloads/installs and over 2500 #github stars !

Thank you to all of our active users and amazing contributors for maintaining such an active community! The future of #matrixprofile is bright

Hi all, I’m a researcher working on brain-computer interfaces at Snap Inc.

My goal is to prove that noninvasive #BCI and #neurofeedback can provide value outside of the lab.

Currently for me that includes (but does not limit to) applying #deeplearning methods to #timeseries data like #EEG, but also other sensors like #eyetracking or inertial sensors (#IMUs)



Also a #python geek. I maintain an M/EEG analysis toolbox
https://github.com/nbara/python-meegkit

#Introduction #Restart
Hi my name is Gregor and I am not migrating from Twitter, but from another Mastodon instance. But still a restart for me.

My job offers me the opportunity to move into the field of #datascience #ai #ml. And as the whole Twitter community seems to come over I want to participate more in the community and immerse myself here.

I am currently focused on #timeseries analysis, but I am very curious about all the other topics as well.

Looking forward to meet you all.

#introduction

Hi, I'm Simon! I'm Data Scientist and #DataEngineer specializing in #remotesensing, #GIS, #spatial, #ecommerce, #timeseries, and duo of #Python + #opensource :)

Living in Europe Now you can catch me in Wrocław and from August 2024 in Helsinki / Vantaa

Creator of #pyinterpolate package: https://pypi.org/project/pyinterpolate/ for Kriging and spatial interpolation

#wsknn package for session-based recommendations: https://pypi.org/project/wsknn/