#timeseries — Public Fediverse posts

Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning https://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

https://openreview.net/forum?id=EUAXc9Hlvm

https://www.santafe.edu/news-center/news/a-simple-baseline-for-ai-forecasting-in-machine-learning

#machineLearning #forecasting #timeseries #forecasting #ML

Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning https://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

https://openreview.net/forum?id=EUAXc9Hlvm

https://www.santafe.edu/news-center/news/a-simple-baseline-for-ai-forecasting-in-machine-learning

#machineLearning #forecasting #timeseries #forecasting #ML

Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning https://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

https://openreview.net/forum?id=EUAXc9Hlvm

https://www.santafe.edu/news-center/news/a-simple-baseline-for-ai-forecasting-in-machine-learning

#machineLearning #forecasting #timeseries #forecasting #ML

Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning https://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

https://openreview.net/forum?id=EUAXc9Hlvm

https://www.santafe.edu/news-center/news/a-simple-baseline-for-ai-forecasting-in-machine-learning

#machineLearning #forecasting #timeseries #forecasting #ML

Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning https://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

https://openreview.net/forum?id=EUAXc9Hlvm

https://www.santafe.edu/news-center/news/a-simple-baseline-for-ai-forecasting-in-machine-learning

#machineLearning #forecasting #timeseries #forecasting #ML

📰 パラメータ4個で710M超えのFoundation Modelに勝った時系列予測手法FLAIRの全貌 (👍 22)

🇬🇧 FLAIR time series forecasting: just 4 parameters outperforming Amazon's 710M-parameter Chronos model. No GPU needed, numpy/scipy only.
🇰🇷 FLAIR 시계열 예측: 파라미터 4개로 710M 파라미터 모델 능가. GPU 불필요, numpy/scipy만으로 구현.

🔗 https://zenn.dev/t_honda/articles/flair-time-series-forecasting

#MachineLearning #TimeSeries #AI #Zenn

📰 パラメータ4個で710M超えのFoundation Modelに勝った時系列予測手法FLAIRの全貌 (👍 22)

🇬🇧 FLAIR time series forecasting: just 4 parameters outperforming Amazon's 710M-parameter Chronos model. No GPU needed, numpy/scipy only.
🇰🇷 FLAIR 시계열 예측: 파라미터 4개로 710M 파라미터 모델 능가. GPU 불필요, numpy/scipy만으로 구현.

🔗 https://zenn.dev/t_honda/articles/flair-time-series-forecasting

#MachineLearning #TimeSeries #AI #Zenn

📰 パラメータ4個で710M超えのFoundation Modelに勝った時系列予測手法FLAIRの全貌 (👍 22)

🇬🇧 FLAIR time series forecasting: just 4 parameters outperforming Amazon's 710M-parameter Chronos model. No GPU needed, numpy/scipy only.
🇰🇷 FLAIR 시계열 예측: 파라미터 4개로 710M 파라미터 모델 능가. GPU 불필요, numpy/scipy만으로 구현.

🔗 https://zenn.dev/t_honda/articles/flair-time-series-forecasting

#MachineLearning #TimeSeries #AI #Zenn

📰 パラメータ4個で710M超えのFoundation Modelに勝った時系列予測手法FLAIRの全貌 (👍 22)

🇬🇧 FLAIR time series forecasting: just 4 parameters outperforming Amazon's 710M-parameter Chronos model. No GPU needed, numpy/scipy only.
🇰🇷 FLAIR 시계열 예측: 파라미터 4개로 710M 파라미터 모델 능가. GPU 불필요, numpy/scipy만으로 구현.

🔗 https://zenn.dev/t_honda/articles/flair-time-series-forecasting

#MachineLearning #TimeSeries #AI #Zenn

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: https://zalt.me/blog/2026/04/prometheus-tsdb-sanity

#Prometheus #TSDB #timeseries #observability

Google's 200M-parameter time-series foundation model with 16k context

https://github.com/google-research/timesfm

#HackerNews #GoogleResearch #TimeSeries #Model #AIInnovation #MachineLearning #ContextualAI

Google's 200M-parameter time-series foundation model with 16k context

https://github.com/google-research/timesfm

#HackerNews #GoogleResearch #TimeSeries #Model #AIInnovation #MachineLearning #ContextualAI

Google's 200M-parameter time-series foundation model with 16k context

https://github.com/google-research/timesfm

#HackerNews #GoogleResearch #TimeSeries #Model #AIInnovation #MachineLearning #ContextualAI

Google's 200M-parameter time-series foundation model with 16k context

https://github.com/google-research/timesfm

#HackerNews #GoogleResearch #TimeSeries #Model #AIInnovation #MachineLearning #ContextualAI

Google's 200M-parameter time-series foundation model with 16k context

https://github.com/google-research/timesfm

#HackerNews #GoogleResearch #TimeSeries #Model #AIInnovation #MachineLearning #ContextualAI

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.
https://github.com/opensearch-project/OpenSearch-Dashboards/issues/11439

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

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.
https://github.com/opensearch-project/OpenSearch-Dashboards/issues/11439

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

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.
https://github.com/opensearch-project/OpenSearch-Dashboards/issues/11439

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

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.
https://github.com/opensearch-project/OpenSearch-Dashboards/issues/11439

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

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.
https://github.com/opensearch-project/OpenSearch-Dashboards/issues/11439

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

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: https://medium.com/write-a-catalyst/time-series-forecasting-analysis-with-python-a8b518e54708

#python #timeSeries #finance #dataScience #forecasting

@programming @ai @socialsciences @pythonclcoding

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: https://medium.com/write-a-catalyst/time-series-forecasting-analysis-with-python-a8b518e54708

#python #timeSeries #finance #dataScience #forecasting

@programming @ai @socialsciences @pythonclcoding

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: https://medium.com/write-a-catalyst/time-series-forecasting-analysis-with-python-a8b518e54708

#python #timeSeries #finance #dataScience #forecasting

@programming @ai @socialsciences @pythonclcoding

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: https://medium.com/write-a-catalyst/time-series-forecasting-analysis-with-python-a8b518e54708

#python #timeSeries #finance #dataScience #forecasting

@programming @ai @socialsciences @pythonclcoding

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: https://medium.com/write-a-catalyst/time-series-forecasting-analysis-with-python-a8b518e54708

#python #timeSeries #finance #dataScience #forecasting

@programming @ai @socialsciences @pythonclcoding

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
https://www.hylkerozema.nl/2026/02/25/five-architectures-for-time-series-forecasting-with-large-language-models/
#DataScience #MachineLearningEngineering #DataScience #Forecasting #FoundationModels #LLM #MachineLearning #TimeSeries #Transformer

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
https://www.hylkerozema.nl/2026/02/25/five-architectures-for-time-series-forecasting-with-large-language-models/
#DataScience #MachineLearningEngineering #DataScience #Forecasting #FoundationModels #LLM #MachineLearning #TimeSeries #Transformer

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
https://www.hylkerozema.nl/2026/02/25/five-architectures-for-time-series-forecasting-with-large-language-models/
#DataScience #MachineLearningEngineering #DataScience #Forecasting #FoundationModels #LLM #MachineLearning #TimeSeries #Transformer

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
https://www.hylkerozema.nl/2026/02/25/five-architectures-for-time-series-forecasting-with-large-language-models/
#DataScience #MachineLearningEngineering #DataScience #Forecasting #FoundationModels #LLM #MachineLearning #TimeSeries #Transformer

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
https://www.hylkerozema.nl/2026/02/25/five-architectures-for-time-series-forecasting-with-large-language-models/
#DataScience #MachineLearningEngineering #DataScience #Forecasting #FoundationModels #LLM #MachineLearning #TimeSeries #Transformer

Learning time series analysis through CDR

https://negativepid.blog/learning-time-series-analysis-through-cdr/

#crimeAnalysis #dataAnalytics #CDR #callRecords #timeSeries #R #digitalInvestigations #OSINT #SOCMINT #HUMINT #socialEngineering #cyberInvestigations #intelligence #darkWeb #negativepid

Learning time series analysis through CDR

https://negativepid.blog/learning-time-series-analysis-through-cdr/

#crimeAnalysis #dataAnalytics #CDR #callRecords #timeSeries #R #digitalInvestigations #OSINT #SOCMINT #HUMINT #socialEngineering #cyberInvestigations #intelligence #darkWeb #negativepid

This is a hands-on walkthrough of building a real-time dashboard with Quarkus + Redis TimeSeries.

Live ingestion via WebSockets, automatic downsampling, multi-resolution queries, and a simple browser UI.
Crypto is just the data source. The patterns apply to metrics, IoT, and event streams.

https://www.the-main-thread.com/p/real-time-crypto-dashboard-java-quarkus-redis-timeseries

#Java #Quarkus #Redis #TimeSeries #BackendEngineering #EventStreaming

This is a hands-on walkthrough of building a real-time dashboard with Quarkus + Redis TimeSeries.

Live ingestion via WebSockets, automatic downsampling, multi-resolution queries, and a simple browser UI.
Crypto is just the data source. The patterns apply to metrics, IoT, and event streams.

https://www.the-main-thread.com/p/real-time-crypto-dashboard-java-quarkus-redis-timeseries

#Java #Quarkus #Redis #TimeSeries #BackendEngineering #EventStreaming

This is a hands-on walkthrough of building a real-time dashboard with Quarkus + Redis TimeSeries.

Live ingestion via WebSockets, automatic downsampling, multi-resolution queries, and a simple browser UI.
Crypto is just the data source. The patterns apply to metrics, IoT, and event streams.

https://www.the-main-thread.com/p/real-time-crypto-dashboard-java-quarkus-redis-timeseries

#Java #Quarkus #Redis #TimeSeries #BackendEngineering #EventStreaming

This is a hands-on walkthrough of building a real-time dashboard with Quarkus + Redis TimeSeries.

Live ingestion via WebSockets, automatic downsampling, multi-resolution queries, and a simple browser UI.
Crypto is just the data source. The patterns apply to metrics, IoT, and event streams.

https://www.the-main-thread.com/p/real-time-crypto-dashboard-java-quarkus-redis-timeseries

#Java #Quarkus #Redis #TimeSeries #BackendEngineering #EventStreaming

This is a hands-on walkthrough of building a real-time dashboard with Quarkus + Redis TimeSeries.

Live ingestion via WebSockets, automatic downsampling, multi-resolution queries, and a simple browser UI.
Crypto is just the data source. The patterns apply to metrics, IoT, and event streams.

https://www.the-main-thread.com/p/real-time-crypto-dashboard-java-quarkus-redis-timeseries

#Java #Quarkus #Redis #TimeSeries #BackendEngineering #EventStreaming

Financial Modeling Series (Data → Analysis → Decision → Action)

A 4-part series to build a finance ML workflow you can validate and run daily.

We go from dataset building → time-aware validation → decision rules → a daily report you can automate in Python.

:medium: https://medium.com/write-a-catalyst/financial-modeling-series-b92548900296

#Finance #MachineLearning #Python #TimeSeries #Quant #ai

@ai @markets @socialsciences @programming @theartificialintelligence @towardsdatascience @medium

Time-series analysis plays a critical role in modern data-driven organizations. By understanding how data changes over time, businesses can detect patterns, forecast outcomes, and respond more effectively to real-world events.

EDB Postgres AI simplifies #timeseries data management by combining scalable storage, performance optimization, and AI integration within a trusted #PostgreSQL environment.

Find out more: https://www.enterprisedb.com/blog/what-is-a-time-series

Time-series analysis plays a critical role in modern data-driven organizations. By understanding how data changes over time, businesses can detect patterns, forecast outcomes, and respond more effectively to real-world events.

EDB Postgres AI simplifies #timeseries data management by combining scalable storage, performance optimization, and AI integration within a trusted #PostgreSQL environment.

Find out more: https://www.enterprisedb.com/blog/what-is-a-time-series

Time-series analysis plays a critical role in modern data-driven organizations. By understanding how data changes over time, businesses can detect patterns, forecast outcomes, and respond more effectively to real-world events.

EDB Postgres AI simplifies #timeseries data management by combining scalable storage, performance optimization, and AI integration within a trusted #PostgreSQL environment.

Find out more: https://www.enterprisedb.com/blog/what-is-a-time-series

Financial Modeling Series: #4 How to Run a Daily Trading Report from an ML Model — Python Solution

A practical daily routine: update features, predict today, apply the threshold rule, and produce a readable decision report.

This post shows a clean pipeline (data → features → prediction → rule → report) you can run every day with Python.

:medium: https://medium.com/@hasanaligultekin/financial-modeling-series-4-how-to-run-a-daily-trading-report-from-an-ml-model-python-solution-a67a41cca2c7

#Python #MachineLearning #Finance #Automation #TimeSeries

@ai @markets @programming @socialsciences @pythonclcoding

Financial Modeling Series: #4 How to Run a Daily Trading Report from an ML Model — Python Solution

A practical daily routine: update features, predict today, apply the threshold rule, and produce a readable decision report.

This post shows a clean pipeline (data → features → prediction → rule → report) you can run every day with Python.

:medium: https://medium.com/@hasanaligultekin/financial-modeling-series-4-how-to-run-a-daily-trading-report-from-an-ml-model-python-solution-a67a41cca2c7

#Python #MachineLearning #Finance #Automation #TimeSeries

@ai @markets @programming @socialsciences @pythonclcoding

Financial Modeling Series: #4 How to Run a Daily Trading Report from an ML Model — Python Solution

A practical daily routine: update features, predict today, apply the threshold rule, and produce a readable decision report.

This post shows a clean pipeline (data → features → prediction → rule → report) you can run every day with Python.

:medium: https://medium.com/@hasanaligultekin/financial-modeling-series-4-how-to-run-a-daily-trading-report-from-an-ml-model-python-solution-a67a41cca2c7

#Python #MachineLearning #Finance #Automation #TimeSeries

@ai @markets @programming @socialsciences @pythonclcoding

Financial Modeling Series: #4 How to Run a Daily Trading Report from an ML Model — Python Solution

A practical daily routine: update features, predict today, apply the threshold rule, and produce a readable decision report.

This post shows a clean pipeline (data → features → prediction → rule → report) you can run every day with Python.

:medium: https://medium.com/@hasanaligultekin/financial-modeling-series-4-how-to-run-a-daily-trading-report-from-an-ml-model-python-solution-a67a41cca2c7

#Python #MachineLearning #Finance #Automation #TimeSeries

@ai @markets @programming @socialsciences @pythonclcoding

Acaba de ser publicado o trabalho "Enhanced Forecasting Model using Transformers, Extended Long-short-term Memory, and Randomized Fuzzy Cognitive Maps", apresentado na 35ª Conferência Brasileira de Sistemas Inteligentes (BRACIS 2025).

O texto está disponível no link https://doi.org/10.1007/978-3-032-15987-8_28 e todo feedback é vem vindo.

#Research #Timeseries #Transformers #deeplearning

Acaba de ser publicado o trabalho "Enhanced Forecasting Model using Transformers, Extended Long-short-term Memory, and Randomized Fuzzy Cognitive Maps", apresentado na 35ª Conferência Brasileira de Sistemas Inteligentes (BRACIS 2025).

O texto está disponível no link https://doi.org/10.1007/978-3-032-15987-8_28 e todo feedback é vem vindo.

#Research #Timeseries #Transformers #deeplearning

Acaba de ser publicado o trabalho "Enhanced Forecasting Model using Transformers, Extended Long-short-term Memory, and Randomized Fuzzy Cognitive Maps", apresentado na 35ª Conferência Brasileira de Sistemas Inteligentes (BRACIS 2025).

O texto está disponível no link https://doi.org/10.1007/978-3-032-15987-8_28 e todo feedback é vem vindo.

#Research #Timeseries #Transformers #deeplearning

Acaba de ser publicado o trabalho "Enhanced Forecasting Model using Transformers, Extended Long-short-term Memory, and Randomized Fuzzy Cognitive Maps", apresentado na 35ª Conferência Brasileira de Sistemas Inteligentes (BRACIS 2025).

O texto está disponível no link https://doi.org/10.1007/978-3-032-15987-8_28 e todo feedback é vem vindo.

#Research #Timeseries #Transformers #deeplearning

Acaba de ser publicado o trabalho "Enhanced Forecasting Model using Transformers, Extended Long-short-term Memory, and Randomized Fuzzy Cognitive Maps", apresentado na 35ª Conferência Brasileira de Sistemas Inteligentes (BRACIS 2025).

O texto está disponível no link https://doi.org/10.1007/978-3-032-15987-8_28 e todo feedback é vem vindo.

#Research #Timeseries #Transformers #deeplearning