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Pinned

  1. Lightning ⚡️ fast forecasting with statistical and econometric models.

    Jupyter Notebook 1.6k 94

  2. Scalable and user friendly neural 🧠 forecasting algorithms for time series data 〰️.

    Jupyter Notebook 921 83

  3. Calculates various features from time series data. Python implementation of the R package tsfeatures.

    Python 138 24

  4. Automated ⚙️ time series forecasting.

    Python 343 23

  5. covidmx Public

    Python API to get information about COVID-19 in México.

    Python 18 7

  6. mapsmx Public

    Create maps of Mexico easily with Python.

    Python 2 1

2,674 contributions in the last year

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Mon Wed Fri

Contribution activity

October 2022

Created 2 repositories

Created a pull request in conda-forge/statsforecast-feedstock that received 6 comments

[FIX] Add tqdm as new dependency

Checklist Used a personal fork of the feedstock to propose changes Bumped the build number (if the version is unchanged) Reset the build numbe…

+7 −4 6 comments
Reviewed 10 pull requests in 5 repositories

Created an issue in Nixtla/hierarchicalforecast that received 1 comment

[BUG] Min Trace method="mint_shrink"

Users have reported issues trying to run MinTrace(method=='mint_shrink'). I suspect that it is related to datasets with time series of distinct le…

1 comment
Started 8 discussions in 3 repositories
Nixtla/hierarchicalforecast
  • v0.2.0
    This contribution was made on Oct 28
  • v0.1.3
    This contribution was made on Oct 25
  • v0.1.2
    This contribution was made on Oct 5
  • v0.1.1
    This contribution was made on Oct 5
Nixtla/statsforecast
  • v1.1.3
    This contribution was made on Oct 25
  • v1.1.2
    This contribution was made on Oct 24
  • v1.1.1
    This contribution was made on Oct 5
Nixtla/neuralforecast
  • v1.0.0
    This contribution was made on Oct 4
Answered 1 discussion in 1 repository
Nixtla/statsforecast
8 contributions in private repositories Oct 3 – Oct 27

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