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TimeGPT-2.1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.

12 mentions on Hacker News by 6 people: 5 stories, 7 comments · first 2021-12-05 · latest 2026-10-19

Mentions per year 12 mentions · busiest year 2024 (5)
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  1. How does it compare to nixtla TimeGPT? https://github.com/Nixtla/nixtla
    comment by amai · 2026-10-19 · in Timestral: Mistral's time-series foundation models
  2. Also: https://github.com/Nixtla/nixtla and https://facebook.github.io/prophet/
    comment by wiradikusuma · 2026-03-31 · in Google's 200M-parameter time-series foundation model with 16k context
  3. …ago (things may have changed) someone showed simple time series models outperforming Chronos. https://github.com/Nixtla/nixtla/tree/main/experiments/amazo... In domains where there is inherent randomness in the process, simple (ensemble)…
    comment by wenc · 2024-12-27 · in Are Language Models Actually Useful for Time Series Forecasting?
  4. …models outperformed it (a highly parametrized transformed model) on the M competition datasets. https://github.com/Nixtla/nixtla/tree/main/experiments/amazo...
    comment by wenc · 2024-03-26 · in Moirai: A time series foundation model for universal forecasting
  5. …race, but it is interesting to see open source comparisons to traditional timeseries strategies: https://github.com/Nixtla/nixtla/tree/main/experiments/amazo... In general, the M-Competitions ( https://forecasters.org/resources/time-series-…
    comment by izyda · 2024-03-22 · in Chronos: Learning the Language of Time Series
  6. …competition data sets. 2. Users can deploy the pipeline in their cloud quickly. We use terraform ( https://github.com/Nixtla/nixtla/tree/main/iac/terraform/aws ), so it is very simple to deploy the pipeline on AWS. We are working to…
    comment by fedegr · 2021-12-06 · in Automated Time Series Processing and Forecasting
  7. …be quickly deployed in the user's cloud using terraform. This allows for less development time. ( https://github.com/Nixtla/nixtla )
    comment by fedegr · 2021-12-06 · in Automated Time Series Processing and Forecasting