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https://github.com/nixtla/coreforecast

Fast implementations of common forecasting routines

https://github.com/nixtla/coreforecast

Science Score: 26.0%

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    Low similarity (11.1%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Fast implementations of common forecasting routines

Basic Info
Statistics
  • Stars: 40
  • Watchers: 5
  • Forks: 7
  • Open Issues: 3
  • Releases: 17
Created over 2 years ago · Last pushed 10 months ago
Metadata Files
Readme License Code of conduct

README.md

Motivation

At Nixtla we have implemented several libraries to deal with time series data. We often have to apply some transformation over all of the series, which can prove time consuming even for simple operations like performing some kind of scaling.

We've used numba to speed up our expensive computations, however that comes with other issues such as cold starts and more dependencies (LLVM). That's why we developed this library, which implements several operators in C++ to transform time series data (or other kind of data that can be thought of as independent groups), with the possibility to use multithreading to get the best performance possible.

You probably won't need to use this library directly but rather use one of our higher level libraries like mlforecast, which will use this library under the hood. If you're interested on using this library directly (only depends on numpy) you should continue reading.

Installation

PyPI

python pip install coreforecast

conda-forge

python conda install -c conda-forge coreforecast

Minimal example

The base data structure is the "grouped array" which holds two numpy 1d arrays:

  • data: values of the series.
  • indptr: series boundaries such that data[indptr[i] : indptr[i + 1]] returns the i-th series. For example, if you have two series of sizes 5 and 10 the indptr would be [0, 5, 15].

```python import numpy as np from coreforecast.grouped_array import GroupedArray

data = np.arange(10) indptr = np.array([0, 3, 10]) ga = GroupedArray(data, indptr) ```

Once you have this structure you can run any of the provided transformations, for example:

```python from coreforecast.lag_transforms import ExpandingMean from coreforecast.scalers import LocalStandardScaler

exp_mean = ExpandingMean(lag=1).transform(ga) scaler = LocalStandardScaler().fit(ga) standardized = scaler.transform(ga) ```

Single-array functions

We've also implemented some functions that work on single arrays, you can refer to the following pages:

Owner

  • Name: Nixtla
  • Login: Nixtla
  • Kind: organization
  • Email: ops@nixtla.io
  • Location: United States of America

Open Source Time Series Ecosystem

GitHub Events

Total
  • Create event: 40
  • Issues event: 3
  • Release event: 3
  • Watch event: 10
  • Delete event: 36
  • Issue comment event: 53
  • Push event: 47
  • Pull request review event: 8
  • Pull request review comment event: 4
  • Pull request event: 73
  • Fork event: 2
Last Year
  • Create event: 40
  • Issues event: 3
  • Release event: 3
  • Watch event: 10
  • Delete event: 36
  • Issue comment event: 53
  • Push event: 47
  • Pull request review event: 8
  • Pull request review comment event: 4
  • Pull request event: 73
  • Fork event: 2

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 7
  • Total pull requests: 158
  • Average time to close issues: 3 days
  • Average time to close pull requests: 2 days
  • Total issue authors: 7
  • Total pull request authors: 8
  • Average comments per issue: 2.0
  • Average comments per pull request: 0.65
  • Merged pull requests: 134
  • Bot issues: 0
  • Bot pull requests: 77
Past Year
  • Issues: 2
  • Pull requests: 87
  • Average time to close issues: 1 day
  • Average time to close pull requests: 1 day
  • Issue authors: 2
  • Pull request authors: 6
  • Average comments per issue: 4.5
  • Average comments per pull request: 1.01
  • Merged pull requests: 68
  • Bot issues: 0
  • Bot pull requests: 64
View more stats
Top Authors
Issue Authors
  • braaannigan (1)
  • ogencoglu (1)
  • nauscj (1)
  • AruparnaMaity (1)
  • MMenchero (1)
  • ilgouli (1)
  • joshdunnlime (1)
Pull Request Authors
  • dependabot[bot] (77)
  • jmoralez (72)
  • tracykteal (2)
  • rpmccarter (2)
  • christophertitchen (2)
  • MMenchero (1)
  • nasaul (1)
  • deven367 (1)
Top Labels
Issue Labels
enhancement (1) feature (1) awaiting response (1)
Pull Request Labels
dependencies (77) github_actions (18) feature (14) fix (8) enhancement (7)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 923,056 last-month
  • Total dependent packages: 4
  • Total dependent repositories: 0
  • Total versions: 17
  • Total maintainers: 1
pypi.org: coreforecast

Fast implementations of common forecasting routines

  • Versions: 17
  • Dependent Packages: 4
  • Dependent Repositories: 0
  • Downloads: 923,056 Last month
Rankings
Dependent packages count: 7.3%
Average: 37.9%
Dependent repos count: 68.4%
Maintainers (1)
capybara
Last synced: 10 months ago

Dependencies

.github/workflows/ci.yaml actions
  • actions/checkout v4 composite
  • actions/checkout v3 composite
  • actions/download-artifact v3 composite
  • actions/setup-python v4 composite
  • actions/upload-artifact v3 composite
  • mamba-org/setup-micromamba v1 composite
  • pypa/cibuildwheel v2.16.2 composite
  • pypa/gh-action-pypi-publish release/v1 composite
environment.yml pypi
pyproject.toml pypi
  • importlib_resources python_version < '3.10'
  • numpy *