home.social

#forecasting — Public Fediverse posts

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

  1. Le 29 janvier, j'assistais pour la première fois aux Entretiens Albert-Kahn.
    « Anticiper et gouverner dans l'incertitude »
    Un sujet qui sonne juste en 2026.👍 👍

    Ce moment m'a laissé une conviction forte : l'incertitude n'empêche pas d'agir. Elle oblige même à mieux agir.

    Lire mon retour détaillé ici 👇
    [linkedin.com/posts/matthieu-co]
    #prospective #forecasting #management #administration #Hautsdeseine

  2. Extreme Coastal Flood [and SLR] Maps For Aotearoa New Zealand
    --
    niwa.co.nz/hazards/coastal-haz <-- shared Earth Sciences New Zealand entry page
    --
    experience.arcgis.com/experien <-- NIWA sea level / coastal flooding web mapping tools
    --
    niwa.co.nz/hazards/riskscape-s <-- shared NZ RiskScape software entry page
    --niwa.co.nz/sites/default/files <-- shared 2023 #NIWA report, ‘Mapping New Zealand’s exposure to coastal flooding and sea-level rise’
    --
    niwa.co.nz/hazards/coastal-sto <-- shared NIWA Coastal storm inundation page
    --
    #GIS #spatial #mapping #NewZealand #spatialdata #opendata #water #hydrography #coast #coastal #flood #flooding #inundation #stormsurge #risk #hazard #forecasting #infrastructure #cost #damage #housing #climatechange #storm #extremeweather #tide #inundation #waves #sealevelrise #SLR #model #modeling #spatialanalysis #spatiotemporal #floodmap #remotesensing #LiDAR #SRTM #regional
    @earth Sciences New Zealand | National Institute of Water & Atmospheric Research (NIWA) | @Ministry for the Environment | Manatū mō te Taiao

  3. Extreme Coastal Flood [and SLR] Maps For Aotearoa New Zealand
    --
    niwa.co.nz/hazards/coastal-haz <-- shared Earth Sciences New Zealand entry page
    --
    experience.arcgis.com/experien <-- NIWA sea level / coastal flooding web mapping tools
    --
    niwa.co.nz/hazards/riskscape-s <-- shared NZ RiskScape software entry page
    --niwa.co.nz/sites/default/files <-- shared 2023 #NIWA report, ‘Mapping New Zealand’s exposure to coastal flooding and sea-level rise’
    --
    niwa.co.nz/hazards/coastal-sto <-- shared NIWA Coastal storm inundation page
    --
    #GIS #spatial #mapping #NewZealand #spatialdata #opendata #water #hydrography #coast #coastal #flood #flooding #inundation #stormsurge #risk #hazard #forecasting #infrastructure #cost #damage #housing #climatechange #storm #extremeweather #tide #inundation #waves #sealevelrise #SLR #model #modeling #spatialanalysis #spatiotemporal #floodmap #remotesensing #LiDAR #SRTM #regional
    @earth Sciences New Zealand | National Institute of Water & Atmospheric Research (NIWA) | @Ministry for the Environment | Manatū mō te Taiao

  4. Extreme Coastal Flood [and SLR] Maps For Aotearoa New Zealand
    --
    niwa.co.nz/hazards/coastal-haz <-- shared Earth Sciences New Zealand entry page
    --
    experience.arcgis.com/experien <-- NIWA sea level / coastal flooding web mapping tools
    --
    niwa.co.nz/hazards/riskscape-s <-- shared NZ RiskScape software entry page
    --niwa.co.nz/sites/default/files <-- shared 2023 #NIWA report, ‘Mapping New Zealand’s exposure to coastal flooding and sea-level rise’
    --
    niwa.co.nz/hazards/coastal-sto <-- shared NIWA Coastal storm inundation page
    --
    #GIS #spatial #mapping #NewZealand #spatialdata #opendata #water #hydrography #coast #coastal #flood #flooding #inundation #stormsurge #risk #hazard #forecasting #infrastructure #cost #damage #housing #climatechange #storm #extremeweather #tide #inundation #waves #sealevelrise #SLR #model #modeling #spatialanalysis #spatiotemporal #floodmap #remotesensing #LiDAR #SRTM #regional
    @earth Sciences New Zealand | National Institute of Water & Atmospheric Research (NIWA) | @Ministry for the Environment | Manatū mō te Taiao

  5. Extreme Coastal Flood [and SLR] Maps For Aotearoa New Zealand
    --
    niwa.co.nz/hazards/coastal-haz <-- shared Earth Sciences New Zealand entry page
    --
    experience.arcgis.com/experien <-- NIWA sea level / coastal flooding web mapping tools
    --
    niwa.co.nz/hazards/riskscape-s <-- shared NZ RiskScape software entry page
    --niwa.co.nz/sites/default/files <-- shared 2023 #NIWA report, ‘Mapping New Zealand’s exposure to coastal flooding and sea-level rise’
    --
    niwa.co.nz/hazards/coastal-sto <-- shared NIWA Coastal storm inundation page
    --
    #GIS #spatial #mapping #NewZealand #spatialdata #opendata #water #hydrography #coast #coastal #flood #flooding #inundation #stormsurge #risk #hazard #forecasting #infrastructure #cost #damage #housing #climatechange #storm #extremeweather #tide #inundation #waves #sealevelrise #SLR #model #modeling #spatialanalysis #spatiotemporal #floodmap #remotesensing #LiDAR #SRTM #regional
    @earth Sciences New Zealand | National Institute of Water & Atmospheric Research (NIWA) | @Ministry for the Environment | Manatū mō te Taiao

  6. Extreme Coastal Flood [and SLR] Maps For Aotearoa New Zealand
    --
    niwa.co.nz/hazards/coastal-haz <-- shared Earth Sciences New Zealand entry page
    --
    experience.arcgis.com/experien <-- NIWA sea level / coastal flooding web mapping tools
    --
    niwa.co.nz/hazards/riskscape-s <-- shared NZ RiskScape software entry page
    --niwa.co.nz/sites/default/files <-- shared 2023 report, ‘Mapping New Zealand’s exposure to coastal flooding and sea-level rise’
    --
    niwa.co.nz/hazards/coastal-sto <-- shared NIWA Coastal storm inundation page
    --

    @earth Sciences New Zealand | National Institute of Water & Atmospheric Research (NIWA) | @Ministry for the Environment | Manatū mō te Taiao

  7. Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning arxiv.org/abs/2505.11349

    Context parroting relies on short stretches of time-series data (or context). As it moves through the time series, it scans for similar patterns or motifs that appeared earlier in the sequence, and uses those patterns to predict what might come

    openreview.net/forum?id=EUAXc9

    santafe.edu/news-center/news/a

    #machineLearning #forecasting #timeseries #forecasting #ML

  8. Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning arxiv.org/abs/2505.11349

    Context parroting relies on short stretches of time-series data (or context). As it moves through the time series, it scans for similar patterns or motifs that appeared earlier in the sequence, and uses those patterns to predict what might come

    openreview.net/forum?id=EUAXc9

    santafe.edu/news-center/news/a

    #machineLearning #forecasting #timeseries #forecasting #ML

  9. Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning arxiv.org/abs/2505.11349

    Context parroting relies on short stretches of time-series data (or context). As it moves through the time series, it scans for similar patterns or motifs that appeared earlier in the sequence, and uses those patterns to predict what might come

    openreview.net/forum?id=EUAXc9

    santafe.edu/news-center/news/a

    #machineLearning #forecasting #timeseries #forecasting #ML

  10. Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning arxiv.org/abs/2505.11349

    Context parroting relies on short stretches of time-series data (or context). As it moves through the time series, it scans for similar patterns or motifs that appeared earlier in the sequence, and uses those patterns to predict what might come

    openreview.net/forum?id=EUAXc9

    santafe.edu/news-center/news/a

    #machineLearning #forecasting #timeseries #forecasting #ML

  11. Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning arxiv.org/abs/2505.11349

    Context parroting relies on short stretches of time-series data (or context). As it moves through the time series, it scans for similar patterns or motifs that appeared earlier in the sequence, and uses those patterns to predict what might come

    openreview.net/forum?id=EUAXc9

    santafe.edu/news-center/news/a

    #machineLearning #forecasting #timeseries #forecasting #ML

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. Time Series Forecasting Analysis with Python
    A practical workflow for finance data: clean the timeline, beat a baseline, and ship forecasts you can monitor.
    This post walks through the real steps: missing dates, outliers, leakage-safe splits, baseline models, better models, and monitoring drift after deployment.

    :medium: medium.com/write-a-catalyst/ti

    #python #timeSeries #finance #dataScience #forecasting

    @programming @ai @socialsciences @pythonclcoding

  18. Time Series Forecasting Analysis with Python
    A practical workflow for finance data: clean the timeline, beat a baseline, and ship forecasts you can monitor.
    This post walks through the real steps: missing dates, outliers, leakage-safe splits, baseline models, better models, and monitoring drift after deployment.

    :medium: medium.com/write-a-catalyst/ti

    #python #timeSeries #finance #dataScience #forecasting

    @programming @ai @socialsciences @pythonclcoding

  19. Time Series Forecasting Analysis with Python
    A practical workflow for finance data: clean the timeline, beat a baseline, and ship forecasts you can monitor.
    This post walks through the real steps: missing dates, outliers, leakage-safe splits, baseline models, better models, and monitoring drift after deployment.

    :medium: medium.com/write-a-catalyst/ti

    #python #timeSeries #finance #dataScience #forecasting

    @programming @ai @socialsciences @pythonclcoding

  20. Time Series Forecasting Analysis with Python
    A practical workflow for finance data: clean the timeline, beat a baseline, and ship forecasts you can monitor.
    This post walks through the real steps: missing dates, outliers, leakage-safe splits, baseline models, better models, and monitoring drift after deployment.

    :medium: medium.com/write-a-catalyst/ti

    #python #timeSeries #finance #dataScience #forecasting

    @programming @ai @socialsciences @pythonclcoding

  21. Time Series Forecasting Analysis with Python
    A practical workflow for finance data: clean the timeline, beat a baseline, and ship forecasts you can monitor.
    This post walks through the real steps: missing dates, outliers, leakage-safe splits, baseline models, better models, and monitoring drift after deployment.

    :medium: medium.com/write-a-catalyst/ti

    #python #timeSeries #finance #dataScience #forecasting

    @programming @ai @socialsciences @pythonclcoding

  22. Hey hey, please join me for my webinar for the International Labour Organization next Wednesday that includes precentation of my R package bpvars 💙🖤
    `
    🌐 Registrations and info: ilo.org/meetings-and-events/fo

  23. Hey hey, please join me for my webinar for the International Labour Organization next Wednesday that includes precentation of my R package bpvars 💙🖤
    `
    🌐 Registrations and info: ilo.org/meetings-and-events/fo

    #bsvars #bpvars #forecasting #labour #un #ilo #rstats

  24. Hey hey, please join me for my webinar for the International Labour Organization next Wednesday that includes precentation of my R package bpvars 💙🖤
    `
    🌐 Registrations and info: ilo.org/meetings-and-events/fo

    #bsvars #bpvars #forecasting #labour #un #ilo #rstats

  25. Hey hey, please join me for my webinar for the International Labour Organization next Wednesday that includes precentation of my R package bpvars 💙🖤
    `
    🌐 Registrations and info: ilo.org/meetings-and-events/fo

    #bsvars #bpvars #forecasting #labour #un #ilo #rstats

  26. Hey hey, please join me for my webinar for the International Labour Organization next Wednesday that includes precentation of my R package bpvars 💙🖤
    `
    🌐 Registrations and info: ilo.org/meetings-and-events/fo

    #bsvars #bpvars #forecasting #labour #un #ilo #rstats

  27. Five Architectures for Time Series Forecasting with Large Language Models
    Large Language Models are increasingly being applied to time series forecasting. Not as chatbots, but as prediction engines that leverage the pattern recognition capabilitie
    hylkerozema.nl/2026/02/25/five
    #DataScience #MachineLearningEngineering #DataScience #Forecasting #FoundationModels #LLM #MachineLearning #TimeSeries #Transformer

  28. Five Architectures for Time Series Forecasting with Large Language Models
    Large Language Models are increasingly being applied to time series forecasting. Not as chatbots, but as prediction engines that leverage the pattern recognition capabilitie
    hylkerozema.nl/2026/02/25/five
    #DataScience #MachineLearningEngineering #DataScience #Forecasting #FoundationModels #LLM #MachineLearning #TimeSeries #Transformer

  29. Five Architectures for Time Series Forecasting with Large Language Models
    Large Language Models are increasingly being applied to time series forecasting. Not as chatbots, but as prediction engines that leverage the pattern recognition capabilitie
    hylkerozema.nl/2026/02/25/five
    #DataScience #MachineLearningEngineering #DataScience #Forecasting #FoundationModels #LLM #MachineLearning #TimeSeries #Transformer

  30. Five Architectures for Time Series Forecasting with Large Language Models
    Large Language Models are increasingly being applied to time series forecasting. Not as chatbots, but as prediction engines that leverage the pattern recognition capabilitie
    hylkerozema.nl/2026/02/25/five
    #DataScience #MachineLearningEngineering #DataScience #Forecasting #FoundationModels #LLM #MachineLearning #TimeSeries #Transformer

  31. Five Architectures for Time Series Forecasting with Large Language Models
    Large Language Models are increasingly being applied to time series forecasting. Not as chatbots, but as prediction engines that leverage the pattern recognition capabilitie
    hylkerozema.nl/2026/02/25/five
    #DataScience #MachineLearningEngineering #DataScience #Forecasting #FoundationModels #LLM #MachineLearning #TimeSeries #Transformer

  32. “While some #companies are still experimenting, a growing number are finding that routine #AnalyticalWork — from #forecasting to financial #modelling — as well as #research and drafting #content, can now be done almost instantly by #SoftwareAgents.

    Many have already introduced specific #tools that have transformed the #work of #professionals in their industry — Harvey in #legal services, Writer for #corporate communications, Synthesia for #training content and Intercom’s Fin for customer #support, for example. Some companies are building their own specialised tools #InHouse.”

    #WhiteCollar / #ZeroHourWork / #Anthropic / #AI / #ArtificialIntelligence <archive.md/qCmsY> / <ft.com/content/92dfd571-8d34-4>