#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.

#Day6 of #30DayChartChallenge, theme: #FlorenceNightingale

Estacionalidad promedio de compraventas de vivienda en España (2007-2024). Coxcomb plot muestra meses con ventas típicamente >100 (altas) o <100 (bajas) vs media anual.

Método: X-13ARIMA-SEATS con #rstats {seasonal}. Datos: INE.

Código: https://t.ly/lC34V

That's a great tutorial Added to my list
https://buff.ly/42s06Yg
#python #pandas #timeseries

VictoriaMetrics Playgrounds! Did you know that #VictoriaMetrics offers interactive #playgrounds to enhance your experience? Test the query engine: Experiment with queries to uncover valuable insights. Ready to explore?
Try today at bit.ly/4hNc9DJ #timeseries #observability #opensource #devtools

VictoriaMetrics Playgrounds!
Did you know that #VictoriaMetrics offers interactive Playgrounds to enhance your experience?

#Test the #query engine: Experiment with queries to uncover valuable insights.
Relabeling playground: Refine and transform your metrics effortlessly.
Whether testing, learning, or optimizing VictoriaMetrics Playgrounds supports your journey to better data insights.

Ready to explore? Try today at https://play.victoriametrics.com
#TimeSeries #Observability #OpenSource

The #AirQualityInMadrid project is complete! I’ve built and deployed a working air quality prediction system using time series models. Learned a lot through the process, from data wrangling to deployment. #MachineLearning #TimeSeries #DataScience #Python #DataTalksClub #zoomcamp #Machinelearning

Now training models on the #AirQualityInMadrid dataset! Using LSTM, ARIMA, and other models with #Torch and #Darts. Excited to see how each model performs for time series forecasting. #MachineLearning #TimeSeries #Python #DataTalksClub #zoomcamp #Machinelearning

Data wrangling complete on the #AirQualityInMadrid dataset. Normalized the features, filled missing values, and reshaped the data for time series forecasting. Ready to train some models now! #DataScience #TimeSeries #MachineLearning #Python #DataTalksClub #zoomcamp #Machinelearning

Completing the EDA phase on the #AirQualityInMadrid dataset. Looking for patterns, handling missing values, and investigating the seasonal variations in air quality. EDA is crucial before we move on to model training. #TimeSeries #DataScience #Python #DataTalksClub #zoomcamp #Machinelearning

Yesterday, it is to say less than two month after #ESA #Sentinel1C launch, a first release of images was made available on #COPERNICUS data space environment to allow users to test their software compatibility.

So, I tested my soft, the AMSTerEngine which is at the core of the AMSTer Toolbox for #InSAR #timeseries processing.

And here is the very first result using a #Sentinel1C - #Sentinel1A image pair acquired on January 17 and 16 respectively on Brest, France.

Consequently, AMSTer Tool box is #Sentinel1C compatible!

This is just normal since #ESA kept the same format for these new data. However, knowing from long that the only reliable stuff in computing is the on/off button, it is quite a relief.

#AMSTerToolbox is freely available here: https://lnkd.in/eGGuY3Pv

I have a #timeseries of values at low temporal resolution where the values represent an average of the respective surrounding intervals.
I wish to up-sample this sequence to a higher temporal resolution in such a way that the average of the up-sampled values is equal to the corresponding value from the original time series.
Does an #algorithm for the kind of interpolation I am looking for exist? (not Pandas' resample or SciPy's signal.resample.) And is there an implementation of it in #Python?

Did you know #VictoriaMetrics offers multiple Playgrounds?
Explore how to:
Test the powerful query engine
Visualize metrics with #Grafana Dashboards
And much more!
Dive into the details
play-grafana.victoriametrics.com

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

Hi @fosdem :
Will say present on February 2nd at 9.10 CET.

Jose Gomez-Selles, our #Cloud Product Lead, will present his talk “Discovering the Magic Behind OpenTelemetry Instrumentation.”

https://fosdem.org/2025/schedule/event/fosdem-2025-4146-discovering-the-magic-behind-opentelemetry-instrumentation/

#python #technology #software
#prediction #statistics
#timeseries #datascience
#automation #modeling #optimization #algorithm
#NIXTLA
Out-of-Sample Time Series Forecasting (TSF) via Nixtla’s StatsForecast: AutoARIMA, AutoETS, AutoCES, AutoTheta & AutoTBATS Models

Learn how to leverage the integrated capabilities of Nixtla’s StatsForecast, an open-source Python project focused on state-of-the-art time series forecasting at scale!

#exploremore

https://medium.com/@alexzap922/out-of-sample-time-series-forecasting-tsf-via-nixtlas-statsforecast-autoarima-autoets-0e163a22d9cb?sk=214947d25d78b26127c6fd9a4eb068c6

Here’s everything you need to know (beyond the standard definition) to master the numerical derivative world. Read more from Piero Paialunga!

#DataScience #TimeSeries #Python

https://towardsdatascience.com/hands-on-numerical-derivative-with-python-from-zero-to-hero-79eb5b5ffabf

Mapping Historical Forest Biomass For Stock-Change Assessments At Parcel To Landscape Scales
--
https://doi.org/10.1016/j.foreco.2023.121348 <-- shared paper
--
https://phys.org/news/2022-06-historical-biomass-key-buffering-climate.html <-- shared technical article
--
https://dx.doi.org/10.1126/science.abk3126 <-- shared paper
--
#GIS #spatial #mapping #Landsat #LiDAR #AbovegroundBiomass #MachineLearning #LandCover #biomass #spatialanalysis #spatiotemporal #historical #parcel #landscape #Landtrendr #landsat #timeseries #machinelearning #algorithm #model #modeling #field #fieldwork #inventory #LiDAR #NewYork #forest #vegetation #carbonstocks #carbonaccounting #temporal #opendata #opensource #climatechange #planning #management

Integrated Topographic Corrections Improve Forest Mapping Using Landsat Imagery
--
https://doi.org/10.1016/j.jag.2022.102716 <-- shared 2022 paper
--
“HIGHLIGHTS:
• [They] evaluated the impacts of topographic correction on forest mapping in the mountains.
• The enhanced C-correction and the physical model reduced topographic effects.
• The corrected Landsat imagery time series resulted in higher accuracy.
• Terrain information improved classification but not as much as topographic correction.
• [They] recommend using topographic correction for forest cover mapping..."
#GIS #spatial #AtmosphericCorrection #IlluminationCondition #LandCover #ModelComparison #TimeSeries #TopographicCorrection #remotesensing #comparasion #topographic #correction #NDVI #forest #vegetation #model #modeling #spatialanalyis #accuracy #forestcover #Russia #Georgia #CaucasusMountains #spatiotemporal #landsat #elevation #DEM

#python #machinelearning #AI
#timeseries #forecasting #technology #model #statistics
#crypto #BTC #datascience

Crypto Taker Buy/Sell Volume Ratio Forecasting with 30 Darts Models

A Comparative Analysis of Various Darts Time Series Forecasting & Hyperparameter Optimization Algorithms to Predict BTC Taker Buy/Sell Volume Ratio

#exploremore

https://medium.com/@alexzap922/taker-buy-sell-volume-ratio-forecasting-with-30-darts-models-58f78464d703?sk=149f0937612cb45ffe182b8a5250ae72