home.social

#timeseries — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #timeseries, aggregated by home.social.

  1. I had a bunch of JSONL files with sensor readings and needed a quick way to check field cardinality and fill rates, filter records, and look up specific entries. Sure, I could have used Databricks, but where’s the fun in that? 😉

    So I ended up building a TUI for viewing and filtering JSONL data, with some basic statistics included.

    github.com/jsynowiec/tav

    #textual #textualize #python #tui #jsonl #dataEngineering #timeSeries

  2. I had a bunch of JSONL files with sensor readings and needed a quick way to check field cardinality and fill rates, filter records, and look up specific entries. Sure, I could have used Databricks, but where’s the fun in that? 😉

    So I ended up building a TUI for viewing and filtering JSONL data, with some basic statistics included.

    github.com/jsynowiec/tav

    #textual #textualize #python #tui #jsonl #dataEngineering #timeSeries

  3. Working with time-series data at scale? “How Prometheus Keeps Its TSDB Sane” breaks down how Prometheus keeps its own storage manageable and safe.

    Read More: zalt.me/blog/2026/04/prometheu

    #Prometheus #TSDB #timeseries #observability

  4. You already know that you can visualize your metrics from #Prometheus in #OpenSearch Dashboard's Discover Metrics experience (if not, check the comments).

    But what if we could add some #AI sauce to detect anomalies and extrapolate forecasts?

    Check out the new RFC for time series #anomalyDetection and #forecasting in @OpenSearchProject and chime in with your feedback.
    github.com/opensearch-project/

    #OpenSearchAmbassador #timeseries #metrics #monitoring #cloudnative
    @Prometheus

  5. You already know that you can visualize your metrics from in Dashboard's Discover Metrics experience (if not, check the comments).

    But what if we could add some sauce to detect anomalies and extrapolate forecasts?

    Check out the new RFC for time series and in @OpenSearchProject and chime in with your feedback.
    github.com/opensearch-project/


    @Prometheus

  6. You already know that you can visualize your metrics from #Prometheus in #OpenSearch Dashboard's Discover Metrics experience (if not, check the comments).

    But what if we could add some #AI sauce to detect anomalies and extrapolate forecasts?

    Check out the new RFC for time series #anomalyDetection and #forecasting in @OpenSearchProject and chime in with your feedback.
    github.com/opensearch-project/

    #OpenSearchAmbassador #timeseries #metrics #monitoring #cloudnative
    @Prometheus

  7. You already know that you can visualize your metrics from #Prometheus in #OpenSearch Dashboard's Discover Metrics experience (if not, check the comments).

    But what if we could add some #AI sauce to detect anomalies and extrapolate forecasts?

    Check out the new RFC for time series #anomalyDetection and #forecasting in @OpenSearchProject and chime in with your feedback.
    github.com/opensearch-project/

    #OpenSearchAmbassador #timeseries #metrics #monitoring #cloudnative
    @Prometheus

  8. You already know that you can visualize your metrics from #Prometheus in #OpenSearch Dashboard's Discover Metrics experience (if not, check the comments).

    But what if we could add some #AI sauce to detect anomalies and extrapolate forecasts?

    Check out the new RFC for time series #anomalyDetection and #forecasting in @OpenSearchProject and chime in with your feedback.
    github.com/opensearch-project/

    #OpenSearchAmbassador #timeseries #metrics #monitoring #cloudnative
    @Prometheus

  9. Improving Forest Loss Mapping In Nepal Using Landtrendr Time-Series And Machine Learning
    --
    doi.org/10.1016/j.rsase.2025.1 <-- share paper
    --
    “HIGHLIGHTS:
    • ViT-based forest mask, multispectral ensemble LandTrendr and terrain shadow mask.
    • District-level RF/XGBoost model training with expert-weighted validation.
    • Outperformed GFC and REDD + AI benchmarks in accuracy and F1 performance.
    • RF excelled in High Mountains/Himalayas; XGBoost in the lower Mountain regions.
    • NBR contributed the most; snow-impacted forest loss uncertainty was observed..."
    #Forestdisturbance #forest #disturbance #remotesensing #LandTrendr #workflow #timeseries #ViT #RF #XGBoost #GEE #Nepal #ForestNepal #spatial #GIS #mapping #earthobservation #landsat #Himalayas #mountains #alpine #vegetation #AI #multispectral #monitoring #spatialanalysis #spatiotemporal #loss #change #machinelearning #NDR #conservation #planning #policy #mitagion #ecology #Karnali #Bagmati, #Darchula #Siwalik #GlobalForestChange #Degradation

  10. Improving Forest Loss Mapping In Nepal Using Landtrendr Time-Series And Machine Learning
    --
    doi.org/10.1016/j.rsase.2025.1 <-- share paper
    --
    “HIGHLIGHTS:
    • ViT-based forest mask, multispectral ensemble LandTrendr and terrain shadow mask.
    • District-level RF/XGBoost model training with expert-weighted validation.
    • Outperformed GFC and REDD + AI benchmarks in accuracy and F1 performance.
    • RF excelled in High Mountains/Himalayas; XGBoost in the lower Mountain regions.
    • NBR contributed the most; snow-impacted forest loss uncertainty was observed..."
    #Forestdisturbance #forest #disturbance #remotesensing #LandTrendr #workflow #timeseries #ViT #RF #XGBoost #GEE #Nepal #ForestNepal #spatial #GIS #mapping #earthobservation #landsat #Himalayas #mountains #alpine #vegetation #AI #multispectral #monitoring #spatialanalysis #spatiotemporal #loss #change #machinelearning #NDR #conservation #planning #policy #mitagion #ecology #Karnali #Bagmati, #Darchula #Siwalik #GlobalForestChange #Degradation

  11. Improving Forest Loss Mapping In Nepal Using Landtrendr Time-Series And Machine Learning
    --
    doi.org/10.1016/j.rsase.2025.1 <-- share paper
    --
    “HIGHLIGHTS:
    • ViT-based forest mask, multispectral ensemble LandTrendr and terrain shadow mask.
    • District-level RF/XGBoost model training with expert-weighted validation.
    • Outperformed GFC and REDD + AI benchmarks in accuracy and F1 performance.
    • RF excelled in High Mountains/Himalayas; XGBoost in the lower Mountain regions.
    • NBR contributed the most; snow-impacted forest loss uncertainty was observed..."
    #Forestdisturbance #forest #disturbance #remotesensing #LandTrendr #workflow #timeseries #ViT #RF #XGBoost #GEE #Nepal #ForestNepal #spatial #GIS #mapping #earthobservation #landsat #Himalayas #mountains #alpine #vegetation #AI #multispectral #monitoring #spatialanalysis #spatiotemporal #loss #change #machinelearning #NDR #conservation #planning #policy #mitagion #ecology #Karnali #Bagmati, #Darchula #Siwalik #GlobalForestChange #Degradation

  12. Improving Forest Loss Mapping In Nepal Using Landtrendr Time-Series And Machine Learning
    --
    doi.org/10.1016/j.rsase.2025.1 <-- share paper
    --
    “HIGHLIGHTS:
    • ViT-based forest mask, multispectral ensemble LandTrendr and terrain shadow mask.
    • District-level RF/XGBoost model training with expert-weighted validation.
    • Outperformed GFC and REDD + AI benchmarks in accuracy and F1 performance.
    • RF excelled in High Mountains/Himalayas; XGBoost in the lower Mountain regions.
    • NBR contributed the most; snow-impacted forest loss uncertainty was observed..."
    #Forestdisturbance #forest #disturbance #remotesensing #LandTrendr #workflow #timeseries #ViT #RF #XGBoost #GEE #Nepal #ForestNepal #spatial #GIS #mapping #earthobservation #landsat #Himalayas #mountains #alpine #vegetation #AI #multispectral #monitoring #spatialanalysis #spatiotemporal #loss #change #machinelearning #NDR #conservation #planning #policy #mitagion #ecology #Karnali #Bagmati, #Darchula #Siwalik #GlobalForestChange #Degradation

  13. Improving Forest Loss Mapping In Nepal Using Landtrendr Time-Series And Machine Learning
    --
    doi.org/10.1016/j.rsase.2025.1 <-- share paper
    --
    “HIGHLIGHTS:
    • ViT-based forest mask, multispectral ensemble LandTrendr and terrain shadow mask.
    • District-level RF/XGBoost model training with expert-weighted validation.
    • Outperformed GFC and REDD + AI benchmarks in accuracy and F1 performance.
    • RF excelled in High Mountains/Himalayas; XGBoost in the lower Mountain regions.
    • NBR contributed the most; snow-impacted forest loss uncertainty was observed..."
    ,

  14. Sử dụng LLM để kiểm tra logic phát hiện bất thường trong dữ liệu giá sản phẩm (ví dụ: tăng giá ảo trước khi giảm giá). LLM giúp phát hiện các trường hợp biên, tạo dữ liệu kiểm thử đối nghịch & làm rõ các giả định trong logic. Rất hữu ích trong giai đoạn thiết kế!

    #LLM #AI #Programming #AnomalyDetection #TimeSeries #VietNam #CôngNghệ #TríTuệNhânTạo

    reddit.com/r/programming/comme

  15. 🎉 Gretl 2025c is here!
    Exciting updates to your favorite econometrics toolkit! Version 2025c brings powerful new features and improvements:

    ✨ New Features:
    Gibbs sampler command for Bayesian analysis is available now!

    🚀 Performance & Quality:
    Faster forward stepwise regression

    🎨 GUI Enhancements:
    Better dbnomics search integration
    Improved dark theme support

    Full changelog:

    gretl.sourceforge.net/ChangeLo

    #gretl #econometrics #opensource #statistics #datascience #economics #timeseries

  16. 📢 Don’t forget!
    Join us today at 6PM CEST for our R-Ladies Rome workshop with @@ramikrispin:
    “Forecasting Time Series with Linear Regression: A Feature-Driven Approach” 📈

    🔗 meetup.com/rladies-rome/events

  17. 📢 Don’t forget!
    Join us today at 6PM CEST for our R-Ladies Rome workshop with @@ramikrispin:
    “Forecasting Time Series with Linear Regression: A Feature-Driven Approach” 📈

    🔗 meetup.com/rladies-rome/events

    #rstats #timeseries #DataScience #RLadiesRome #Rusers #MachineLearning #OpenSource

  18. #binjr has a new home on the Fediverse!

    The social account for binjr on the Fediverse is now hosted on its own dedicated and independently run instance: https://social.binjr.eu.
    (Shout out to the great folks at @gotosocial for making this so easy with their incredibly lightweight ActivityPub server. Go check it out!)

    If you were already following the old @binjr account: no worries! There’s nothing for you to do: your subscription should have automatically carried over to the new account.

    Of course, our brand new lil' instance is quite expectedly off to some rather modest beginnings—it is not open for registration to new users (that is not the point) but it would help "prime the federation pump" if folks from all parts of the Fediverse interacted with it, just once.
    So please consider boosting this, if you feel so inclined!

    Cheers!

    #foss #timeseries #dataviz #java #javafx

  19. #binjr has a new home on the Fediverse!

    The social account for binjr on the Fediverse is now hosted on its own dedicated and independently run instance: https://social.binjr.eu.
    (Shout out to the great folks at @gotosocial for making this so easy with their incredibly lightweight ActivityPub server. Go check it out!)

    If you were already following the old @binjr account: no worries! There’s nothing for you to do: your subscription should have automatically carried over to the new account.

    Of course, our brand new lil' instance is quite expectedly off to some rather modest beginnings—it is not open for registration to new users (that is not the point) but it would help "prime the federation pump" if folks from all parts of the Fediverse interacted with it, just once.
    So please consider boosting this, if you feel so inclined!

    Cheers!

    #foss #timeseries #dataviz #java #javafx

  20. Integrated Topographic Corrections Improve Forest Mapping Using Landsat Imagery
    --
    doi.org/10.1016/j.jag.2022.102 <-- 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

  21. Integrated Topographic Corrections Improve Forest Mapping Using Landsat Imagery
    --
    doi.org/10.1016/j.jag.2022.102 <-- 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

  22. Integrated Topographic Corrections Improve Forest Mapping Using Landsat Imagery
    --
    doi.org/10.1016/j.jag.2022.102 <-- 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

  23. Integrated Topographic Corrections Improve Forest Mapping Using Landsat Imagery
    --
    doi.org/10.1016/j.jag.2022.102 <-- 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

  24. Integrated Topographic Corrections Improve Forest Mapping Using Landsat Imagery
    --
    doi.org/10.1016/j.jag.2022.102 <-- 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..."

  25. We got a #paper out! We demonstrate that you can retrieve **all** of PROSAIL #RadiativeTransfer model parameters from #timeseries of #Sentinel2 #reflectance. You can then even explain ~90% of the full #Hyperspectral data. At least over #crops doi.org/10.1016/j.rse.2024.114

  26. We got a #paper out! We demonstrate that you can retrieve **all** of PROSAIL #RadiativeTransfer model parameters from #timeseries of #Sentinel2 #reflectance. You can then even explain ~90% of the full #Hyperspectral data. At least over #crops doi.org/10.1016/j.rse.2024.114

  27. 3.20 is now available! 🎉

    In this release:
    * Quality-of-life improvements for users of the CSV adapter, like the ability to reload the file from disk using Ctrl+F5, or the possibility to relax the parsing rules and ignore lines with misformatted time stamps.
    * Updates to the latest release of and
    * Many bugs fixed, some particularly old and nagging!

    And more! Read the full changelog and download it at binjr.eu

  28. 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!
    youtube.com/watch?v=yCJr49GU9y
    #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?

    #HLM #TimeSeries

  29. 3.19 is now available! 🎉

    The main feature for this update, is that it does *not* put your machine into an endless cycle of BSOD!, (err... sorry was that too soon 😬)

    Aside from that, it's mostly fixes and updated dependencies, like the most recent security update for and .

    Read the full changelog and download it at binjr.eu

  30. 3.18 is now available! 🎉

    The highlight of this release is the addition of an option to dynamically set the application's theme according to the system's settings.

    Read the full changelog and download it at binjr.eu

  31. binjr 3.17 is now available!

    It is now possible to set a custom zoom factor for the binjr window, there are new options to deal with badly formatted CSV files, and the embedded and runtimes have been updated to 22.

    Read the full changelog and download it at binjr.eu

  32. There are new ways to install and keep it up-to-date! 🎉

    For users, you can get it from the : aur.archlinux.org/packages/bin

    And for users, it is now available via : just type `winget install binjr` and you're good to go!

    These new options come in addition to the existing repos for and based distros, available at repos.binjr.eu/

    And if all else fails, get your binaries straight from binjr.eu/download/latest_relea

  33. There's a new blog post that discusses upcoming features in the next release of binjr!
    TL;DR: New graphics settings are coming to binjr.

    Find out more and give feedback here:
    binjr.eu/blog/2024/02/new-grap

  34. binjr 3.16 is now available!

    Most of the changes are solely under the hood for this release; bug fixes and updates to dependencies.

    Read the full changelog and download it at binjr.eu

  35. binjr 3.15 is now available!

    It adds options on how to represent missing values on charts, better progress notifications while the app is being updated and, of course, the usual dependency updates and bug fixes.

    Read the full changelog and download it at binjr.eu

  36. binjr 3.14 is now available!

    This releases features an update to the embedded Java runtime to as well as a couple of "quality of life" improvements, like the ability set a preference for default chart type and search fields becoming active as soon as they are shown.

    Read the full changelog and download it at binjr.eu

  37. binjr 3.13 is now available!

    This release features the new data adapter that was revealed some time ago, alongside a few UI enhancements to better support it and some most welcome bug fixes, of course.

    Read the full changelog and download it at binjr.eu

  38. The next release of binjr will feature a new data adapter for JDK Flight Recorder files!

    It is still under development but you can give it a try and share your feedback via this github discussion: github.com/orgs/binjr/discussi

  39. This place is now the only home for on social media (well, its *real* home is wherever anyone clones a repo from github.com/binjr/binjr/ I guess):
    as of now, the account over on Twitter/whatever-its-called-this-week will no longer be updated.

    It's all a bit quiet around here for the moment though, so please follow and boost this post and help spread the word, if you feel so inclined!

  40. binjr 3.12 is now available!

    This is a pretty featureful release, whose highlight is a new indexing mode for log files that makes is easy to search for arbitrary strings of character without the need for wildcards while maintaining the same level of performance, even on very large files.

    It also features several other smaller enhancements and many bug fixes: full release notes available at binjr.eu/download/CHANGELOG/#b

  41. #Midjourney generated #illustrations using prompts from #science paper abstracts.

    1-disentangle the interacting drivers of solute loading to streams

    2-Anomalous patterns occurring in hydrological time series data

    3-Salt marshes

    4-space-for-time substitution

    #TimeSeries #SciComm #CriticalZone

    Outtakes from content created during #AGU22.