home.social

#forecasting β€” Public Fediverse posts

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

  1. 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, β€˜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

  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, β€˜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, β€˜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, β€˜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 report, β€˜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

  6. 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

  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. 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

  12. 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

  13. 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

  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. 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

  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. 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

  22. 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

  23. 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

  24. 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

  25. 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

  26. Absolutely gaga over this new preprint by Nick Clark and the @weecology group. So many methodological threads - long-term ecological monitoring, an open data system, careful semi-parametric models, simulation-based inference and forecasting rigor - combine into predicting complex multispecies dynamics while learning about their relationships + drivers

    ecoevorxiv.org/repository/view, code at github.com/nicholasjclark/port

    Thread from Nick at: twitter.com/nj_clark/status/16

    #ecology #forecasting #EFI #mgcv #rstats