I’m a hard-data person. I understand the importance of data. But I also understand the limitations of data. And public opinion timeseries are relatively limited data. It’s going to be hard to prove anything from the charts being posted.
November 6, 2026 at 4:52 PM
I’m a hard-data person. I understand the importance of data. But I also understand the limitations of data. And public opinion timeseries are relatively limited data. It’s going to be hard to prove anything from the charts being posted.
Below I’ve plotted ALL the wastewater timeseries for the available states (this isn’t available on the dashboard):
November 9, 2026 at 8:41 AM
Below I’ve plotted ALL the wastewater timeseries for the available states (this isn’t available on the dashboard):
#Timeseries #prediction #model by @mistralai.bsky.social
#OpenSource Modified MIT lic
#SLM #AI #time-series
#OpenSource Modified MIT lic
#SLM #AI #time-series
Timestral - a mistralai Collection
Time series foundation models for zero-shot probabilistic forecasting.
huggingface.co
November 5, 2026 at 6:33 PM
#Timeseries #prediction #model by @mistralai.bsky.social
#OpenSource Modified MIT lic
#SLM #AI #time-series
#OpenSource Modified MIT lic
#SLM #AI #time-series
Today's "Andy at Sea" Database Seminar: Eventual co-founders Jay Chia and Rohit Kulshreshtha will present their Daft engine for robot data. Public Zoom talk at 4:30pm ET. YouTube video available after: db.cs.cmu.edu/events/andy-...
[Andy at Sea] Database for Visual Data and Robot Sensors: Beyond Vector Search, RAG and Timeseries - Carnegie Mellon Database Group
Robotics produces high-volume time series alongside video and lidar, with different sampling... Read More +
db.cs.cmu.edu
November 9, 2026 at 12:57 PM
Today's "Andy at Sea" Database Seminar: Eventual co-founders Jay Chia and Rohit Kulshreshtha will present their Daft engine for robot data. Public Zoom talk at 4:30pm ET. YouTube video available after: db.cs.cmu.edu/events/andy-...
Postscript:
Lest someone take this post as all criticism, it's not; I think that what @jlerollblues.bsky.social has done is not a bad first attempt at plotting the real wastewater RNA concentration timeseries for the US, corrected for N1 mutation detection problems.
Lest someone take this post as all criticism, it's not; I think that what @jlerollblues.bsky.social has done is not a bad first attempt at plotting the real wastewater RNA concentration timeseries for the US, corrected for N1 mutation detection problems.
November 6, 2026 at 6:23 AM
Postscript:
Lest someone take this post as all criticism, it's not; I think that what @jlerollblues.bsky.social has done is not a bad first attempt at plotting the real wastewater RNA concentration timeseries for the US, corrected for N1 mutation detection problems.
Lest someone take this post as all criticism, it's not; I think that what @jlerollblues.bsky.social has done is not a bad first attempt at plotting the real wastewater RNA concentration timeseries for the US, corrected for N1 mutation detection problems.
I haven’t tried to reproduce @jlerollblues.bsky.social's timeseries, but I’ve observed similar variation in my attempts to aggregate the NWSS wastewater concentration data by state.
November 9, 2026 at 8:41 AM
I haven’t tried to reproduce @jlerollblues.bsky.social's timeseries, but I’ve observed similar variation in my attempts to aggregate the NWSS wastewater concentration data by state.
we have local stations, and there are timeseries covering many metropolitan areas on ESA servers, I can pull it and do the analysis I need.
But I am used to it, and a tool that could simplify this analysis is such as that one on the link should cover more large areas in the southern hemisphere
But I am used to it, and a tool that could simplify this analysis is such as that one on the link should cover more large areas in the southern hemisphere
November 9, 2026 at 11:23 AM
we have local stations, and there are timeseries covering many metropolitan areas on ESA servers, I can pull it and do the analysis I need.
But I am used to it, and a tool that could simplify this analysis is such as that one on the link should cover more large areas in the southern hemisphere
But I am used to it, and a tool that could simplify this analysis is such as that one on the link should cover more large areas in the southern hemisphere
Here I’d like to get in to the state levels.
Some comments.
(Comment 1) The dashboard allows one to view the wastewater timeseries for each state (check the box at top). Nice!
Some comments.
(Comment 1) The dashboard allows one to view the wastewater timeseries for each state (check the box at top). Nice!
November 9, 2026 at 8:41 AM
Here I’d like to get in to the state levels.
Some comments.
(Comment 1) The dashboard allows one to view the wastewater timeseries for each state (check the box at top). Nice!
Some comments.
(Comment 1) The dashboard allows one to view the wastewater timeseries for each state (check the box at top). Nice!
A visitor to the dashboard might have a look at an individual state’s timeseries, such as these for Colorado (left) and Oregon (right).
But they tell two very different pictures!
But they tell two very different pictures!
November 9, 2026 at 8:41 AM
A visitor to the dashboard might have a look at an individual state’s timeseries, such as these for Colorado (left) and Oregon (right).
But they tell two very different pictures!
But they tell two very different pictures!
I was exploring with ufos.microcosm.blue, and atpotato.com/weather/ is also pretty cool
🛸 UFOs: Explore atproto lexicons
Sample records and timeseries statistics for *every* collection seen in the ATmosphere.
ufos.microcosm.blue
November 7, 2026 at 9:48 PM
I was exploring with ufos.microcosm.blue, and atpotato.com/weather/ is also pretty cool
it's okayish at timeseries, can't say I'd super recommend it there
November 2, 2026 at 5:33 AM
it's okayish at timeseries, can't say I'd super recommend it there
I analyze empirical timeseries data of this sort from biology, and unlike physics we don’t assume an error model (or we do, it’s just homogeneous, and Gaussian or L1).
November 6, 2026 at 5:09 PM
I analyze empirical timeseries data of this sort from biology, and unlike physics we don’t assume an error model (or we do, it’s just homogeneous, and Gaussian or L1).
Based on the latest revised BLS nonfarm payroll data shown in the chart, the U.S. added approximately *1.46 million* payroll jobs in 2024. Compared with about *116,000* payroll jobs in 2025.
Meaning 2024 added roughly 1.34 million more payroll jobs than 2025.
data.bls.gov/timeseries/C...
Meaning 2024 added roughly 1.34 million more payroll jobs than 2025.
data.bls.gov/timeseries/C...
Bureau of Labor Statistics Data
data.bls.gov
November 9, 2026 at 10:24 PM
Based on the latest revised BLS nonfarm payroll data shown in the chart, the U.S. added approximately *1.46 million* payroll jobs in 2024. Compared with about *116,000* payroll jobs in 2025.
Meaning 2024 added roughly 1.34 million more payroll jobs than 2025.
data.bls.gov/timeseries/C...
Meaning 2024 added roughly 1.34 million more payroll jobs than 2025.
data.bls.gov/timeseries/C...
Zheng Wang, Kaixuan Zhang, Xiaonan Lu, Wanfang Chen: Echoes of Error: Residual-Directional Local Rollout Consistency for Timeseries Forecasting https://arxiv.org/abs/2611.07473 https://arxiv.org/pdf/2611.07473 https://arxiv.org/html/2611.07473
November 6, 2026 at 6:43 AM
Zheng Wang, Kaixuan Zhang, Xiaonan Lu, Wanfang Chen: Echoes of Error: Residual-Directional Local Rollout Consistency for Timeseries Forecasting https://arxiv.org/abs/2611.07473 https://arxiv.org/pdf/2611.07473 https://arxiv.org/html/2611.07473
Eclipse JNoSQL 1.2.0 adds vector database support for semantic search, RAG, and AI, plus five new TimeSeries operations for Java developers.
November 5, 2026 at 11:35 AM
Eclipse JNoSQL 1.2.0 adds vector database support for semantic search, RAG, and AI, plus five new TimeSeries operations for Java developers.
Yes there is:
doi.org/10.1038/s432...
some refinements of this method have since been made, manuscript currently in prep, stay tuned :)
doi.org/10.1038/s432...
some refinements of this method have since been made, manuscript currently in prep, stay tuned :)
A traceable global warming record and clarity for the 1.5 °C and well-below-2 °C goals - Communications Earth & Environment
Anthropogenic global warming is set to exceed 1.5 °C by 2028 and hence improved quantification of the Paris climate goals is proposed, based on 20-year means of a benchmark timeseries from 1850 to 203...
doi.org
November 6, 2026 at 11:01 AM
Yes there is:
doi.org/10.1038/s432...
some refinements of this method have since been made, manuscript currently in prep, stay tuned :)
doi.org/10.1038/s432...
some refinements of this method have since been made, manuscript currently in prep, stay tuned :)
The latest update for #VictoriaMetrics includes "What's New in VictoriaMetrics #Cloud Q3 2026? VictoriaTraces, a Revamped Overview and #Monitoring, Alerting for Logs and Traces, and a new Beta coming soon".
#DevOps #TimeSeries #OpenSource https://opsmtrs.com/3Lk7JnI
#DevOps #TimeSeries #OpenSource https://opsmtrs.com/3Lk7JnI
VictoriaMetrics
VictoriaMetrics is a fast, cost-effective and scalable open source monitoring solution and time series database typically used for processing high volumes of data and for long term data storage.
opsmtrs.com
November 5, 2026 at 4:14 AM
The latest update for #VictoriaMetrics includes "What's New in VictoriaMetrics #Cloud Q3 2026? VictoriaTraces, a Revamped Overview and #Monitoring, Alerting for Logs and Traces, and a new Beta coming soon".
#DevOps #TimeSeries #OpenSource https://opsmtrs.com/3Lk7JnI
#DevOps #TimeSeries #OpenSource https://opsmtrs.com/3Lk7JnI
Halloween 2026 is in the books! Final trick-or-treater count was 643, our biggest year yet!
Here's the timeseries (collected with Tally by @agiletortoise.bsky.social, visualized in Google sheets). 7-8pm was a madhouse!
Here's the timeseries (collected with Tally by @agiletortoise.bsky.social, visualized in Google sheets). 7-8pm was a madhouse!
November 2, 2026 at 7:37 PM
Halloween 2026 is in the books! Final trick-or-treater count was 643, our biggest year yet!
Here's the timeseries (collected with Tally by @agiletortoise.bsky.social, visualized in Google sheets). 7-8pm was a madhouse!
Here's the timeseries (collected with Tally by @agiletortoise.bsky.social, visualized in Google sheets). 7-8pm was a madhouse!
売上や売れ行きの予測（時系列データ分析）をAIでやっている人に聞きたいです！！
IBMの「Granite Timeseries Ensemble」のように、複数の予測モデルを組み合わせる手法って、実際の業務で試したことありますか？
「こういうデータで予測精度が上がった！」など、あなたの体験や感想をぜひ一言で教えてください✨
#IBMGranite
IBMの「Granite Timeseries Ensemble」のように、複数の予測モデルを組み合わせる手法って、実際の業務で試したことありますか？
「こういうデータで予測精度が上がった！」など、あなたの体験や感想をぜひ一言で教えてください✨
#IBMGranite
November 8, 2026 at 8:38 AM
売上や売れ行きの予測（時系列データ分析）をAIでやっている人に聞きたいです！！
IBMの「Granite Timeseries Ensemble」のように、複数の予測モデルを組み合わせる手法って、実際の業務で試したことありますか？
「こういうデータで予測精度が上がった！」など、あなたの体験や感想をぜひ一言で教えてください✨
#IBMGranite
IBMの「Granite Timeseries Ensemble」のように、複数の予測モデルを組み合わせる手法って、実際の業務で試したことありますか？
「こういうデータで予測精度が上がった！」など、あなたの体験や感想をぜひ一言で教えてください✨
#IBMGranite
Contrastive Time Series Forecasting with Anomalies
Joel Ekstrand, Zahra Taghiyarrenani, Slawomir Nowaczyk
Action editor: Han-Jia Ye
https://openreview.net/forum?id=WLtcRXqHT2
#forecasting #forecast #timeseries
Joel Ekstrand, Zahra Taghiyarrenani, Slawomir Nowaczyk
Action editor: Han-Jia Ye
https://openreview.net/forum?id=WLtcRXqHT2
#forecasting #forecast #timeseries
November 3, 2026 at 5:26 PM
Contrastive Time Series Forecasting with Anomalies
Joel Ekstrand, Zahra Taghiyarrenani, Slawomir Nowaczyk
Action editor: Han-Jia Ye
https://openreview.net/forum?id=WLtcRXqHT2
#forecasting #forecast #timeseries
Joel Ekstrand, Zahra Taghiyarrenani, Slawomir Nowaczyk
Action editor: Han-Jia Ye
https://openreview.net/forum?id=WLtcRXqHT2
#forecasting #forecast #timeseries
The latest update for #Last9 includes "Automated Alert Triage: Route Alerts to the Root-Cause Team" and "Wide Events vs Metrics, Logs and Traces: What #Observability 2.0 Costs".
#Monitoring #TimeSeries #SRE #DevOps https://opsmtrs.com/41EhXHh
#Monitoring #TimeSeries #SRE #DevOps https://opsmtrs.com/41EhXHh
Last9
Last9 provides tools to improve Reliability in large-scale cloud-native environments.
opsmtrs.com
November 5, 2026 at 5:22 AM
The latest update for #Last9 includes "Automated Alert Triage: Route Alerts to the Root-Cause Team" and "Wide Events vs Metrics, Logs and Traces: What #Observability 2.0 Costs".
#Monitoring #TimeSeries #SRE #DevOps https://opsmtrs.com/41EhXHh
#Monitoring #TimeSeries #SRE #DevOps https://opsmtrs.com/41EhXHh
🎲 Testing update:
- redis-8.12~rc2-1.module_redis.8.12
- redis-bloom-8.11.90-1.module_redis.8.12
- redis-json-8.11.90-1.module_redis.8.12
- redis-timeseries-8.11.90-1.module_redis.8.12
github.com/redis/redis/...
- redis-8.12~rc2-1.module_redis.8.12
- redis-bloom-8.11.90-1.module_redis.8.12
- redis-json-8.11.90-1.module_redis.8.12
- redis-timeseries-8.11.90-1.module_redis.8.12
github.com/redis/redis/...
Releases · redis/redis
For developers, who are building real-time data-driven applications, Redis is the preferred, fastest, and most feature-rich cache, data structure server, and document and vector query engine. - red...
github.com
November 3, 2026 at 4:13 PM
🎲 Testing update:
- redis-8.12~rc2-1.module_redis.8.12
- redis-bloom-8.11.90-1.module_redis.8.12
- redis-json-8.11.90-1.module_redis.8.12
- redis-timeseries-8.11.90-1.module_redis.8.12
github.com/redis/redis/...
- redis-8.12~rc2-1.module_redis.8.12
- redis-bloom-8.11.90-1.module_redis.8.12
- redis-json-8.11.90-1.module_redis.8.12
- redis-timeseries-8.11.90-1.module_redis.8.12
github.com/redis/redis/...
📊 27.5% of British retail sales were made online in 2025, up from 3.4% in 2007.
The share peaked at 30.7% in 2021 and has been about 27% a year since 2022.
Source: ONS https://www.ons.gov.uk/businessindustryandtrade/retailindustry/timeseries/j4mc/drsi
The share peaked at 30.7% in 2021 and has been about 27% a year since 2022.
Source: ONS https://www.ons.gov.uk/businessindustryandtrade/retailindustry/timeseries/j4mc/drsi
November 10, 2026 at 4:02 PM
📊 27.5% of British retail sales were made online in 2025, up from 3.4% in 2007.
The share peaked at 30.7% in 2021 and has been about 27% a year since 2022.
Source: ONS https://www.ons.gov.uk/businessindustryandtrade/retailindustry/timeseries/j4mc/drsi
The share peaked at 30.7% in 2021 and has been about 27% a year since 2022.
Source: ONS https://www.ons.gov.uk/businessindustryandtrade/retailindustry/timeseries/j4mc/drsi
New version of Confluent Platform for Apache #Flink this week! The new feature that excites me most is how easy we've made it to use timeseries models.
Compared with the effort it took to do the same sort of thing last year (dalelane.co.uk/blog/?p=5612) the difference is huge
Compared with the effort it took to do the same sort of thing last year (dalelane.co.uk/blog/?p=5612) the difference is huge
Hybrid Demo: Confluent Platform (CP) Flink and Confluent Assistant
YouTube video by Confluent, an IBM Company
youtu.be
November 5, 2026 at 10:50 AM
New version of Confluent Platform for Apache #Flink this week! The new feature that excites me most is how easy we've made it to use timeseries models.
Compared with the effort it took to do the same sort of thing last year (dalelane.co.uk/blog/?p=5612) the difference is huge
Compared with the effort it took to do the same sort of thing last year (dalelane.co.uk/blog/?p=5612) the difference is huge