coreforecast

Fast implementations of common forecasting routines

These details have been verified by PyPI

Project links

GitHub Statistics

Maintainers

capybara

These details have not been verified by PyPI

Development Status
- 3 - Alpha
Intended Audience
- Science/Research
License
- OSI Approved :: Apache Software License
Natural Language
- English
Programming Language

Project description

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

pip install coreforecast

conda-forge

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].

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:

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:

Project details

These details have been verified by PyPI

Project links

GitHub Statistics

Maintainers

capybara

These details have not been verified by PyPI

Development Status
- 3 - Alpha
Intended Audience
- Science/Research
License
- OSI Approved :: Apache Software License
Natural Language
- English
Programming Language

Release history Release notifications | RSS feed

0.0.15

Nov 15, 2024

0.0.14

Nov 5, 2024

This version

0.0.13.1

Oct 16, 2024

0.0.13

Oct 14, 2024

0.0.12

Jul 17, 2024

0.0.11

Jul 17, 2024

0.0.10

Jun 14, 2024

0.0.9

May 7, 2024

0.0.8

Apr 10, 2024

0.0.7

Feb 28, 2024

0.0.6

Feb 20, 2024

0.0.5

Feb 15, 2024

0.0.4

Feb 12, 2024

0.0.3

Nov 29, 2023

0.0.2

Nov 3, 2023

0.0.1

Oct 11, 2023

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

coreforecast-0.0.13.1.tar.gz (87.1 kB view details)

Uploaded Oct 16, 2024 Source

Built Distributions

coreforecast-0.0.13.1-cp313-cp313-win_amd64.whl (189.0 kB view details)

Uploaded Oct 16, 2024 CPython 3.13 Windows x86-64

coreforecast-0.0.13.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (273.7 kB view details)

Uploaded Oct 16, 2024 CPython 3.13 manylinux: glibc 2.17+ x86-64

coreforecast-0.0.13.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (249.9 kB view details)

Uploaded Oct 16, 2024 CPython 3.13 manylinux: glibc 2.17+ ARM64

coreforecast-0.0.13.1-cp313-cp313-macosx_11_0_arm64.whl (196.0 kB view details)

Uploaded Oct 16, 2024 CPython 3.13 macOS 11.0+ ARM64

coreforecast-0.0.13.1-cp313-cp313-macosx_10_13_x86_64.whl (248.8 kB view details)

Uploaded Oct 16, 2024 CPython 3.13 macOS 10.13+ x86-64

coreforecast-0.0.13.1-cp312-cp312-win_amd64.whl (188.9 kB view details)

Uploaded Oct 16, 2024 CPython 3.12 Windows x86-64

coreforecast-0.0.13.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (274.8 kB view details)

Uploaded Oct 16, 2024 CPython 3.12 manylinux: glibc 2.17+ x86-64

coreforecast-0.0.13.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (250.0 kB view details)

Uploaded Oct 16, 2024 CPython 3.12 manylinux: glibc 2.17+ ARM64

coreforecast-0.0.13.1-cp312-cp312-macosx_11_0_arm64.whl (196.0 kB view details)

Uploaded Oct 16, 2024 CPython 3.12 macOS 11.0+ ARM64

coreforecast-0.0.13.1-cp312-cp312-macosx_10_13_x86_64.whl (248.6 kB view details)

Uploaded Oct 16, 2024 CPython 3.12 macOS 10.13+ x86-64

coreforecast-0.0.13.1-cp311-cp311-win_amd64.whl (188.1 kB view details)

Uploaded Oct 16, 2024 CPython 3.11 Windows x86-64

coreforecast-0.0.13.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (272.4 kB view details)

Uploaded Oct 16, 2024 CPython 3.11 manylinux: glibc 2.17+ x86-64

coreforecast-0.0.13.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (248.2 kB view details)

Uploaded Oct 16, 2024 CPython 3.11 manylinux: glibc 2.17+ ARM64

coreforecast-0.0.13.1-cp311-cp311-macosx_11_0_arm64.whl (196.9 kB view details)

Uploaded Oct 16, 2024 CPython 3.11 macOS 11.0+ ARM64

coreforecast-0.0.13.1-cp311-cp311-macosx_10_9_x86_64.whl (248.7 kB view details)

Uploaded Oct 16, 2024 CPython 3.11 macOS 10.9+ x86-64

coreforecast-0.0.13.1-cp310-cp310-win_amd64.whl (186.9 kB view details)

Uploaded Oct 16, 2024 CPython 3.10 Windows x86-64

coreforecast-0.0.13.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (271.4 kB view details)

Uploaded Oct 16, 2024 CPython 3.10 manylinux: glibc 2.17+ x86-64

coreforecast-0.0.13.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (247.2 kB view details)

Uploaded Oct 16, 2024 CPython 3.10 manylinux: glibc 2.17+ ARM64

coreforecast-0.0.13.1-cp310-cp310-macosx_11_0_arm64.whl (195.4 kB view details)

Uploaded Oct 16, 2024 CPython 3.10 macOS 11.0+ ARM64

coreforecast-0.0.13.1-cp310-cp310-macosx_10_9_x86_64.whl (247.2 kB view details)

Uploaded Oct 16, 2024 CPython 3.10 macOS 10.9+ x86-64

coreforecast-0.0.13.1-cp39-cp39-win_amd64.whl (182.9 kB view details)

Uploaded Oct 16, 2024 CPython 3.9 Windows x86-64

coreforecast-0.0.13.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (271.4 kB view details)

Uploaded Oct 16, 2024 CPython 3.9 manylinux: glibc 2.17+ x86-64

coreforecast-0.0.13.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (247.4 kB view details)

Uploaded Oct 16, 2024 CPython 3.9 manylinux: glibc 2.17+ ARM64

coreforecast-0.0.13.1-cp39-cp39-macosx_11_0_arm64.whl (195.5 kB view details)

Uploaded Oct 16, 2024 CPython 3.9 macOS 11.0+ ARM64

coreforecast-0.0.13.1-cp39-cp39-macosx_10_9_x86_64.whl (247.3 kB view details)

Uploaded Oct 16, 2024 CPython 3.9 macOS 10.9+ x86-64

File details

Details for the file coreforecast-0.0.13.1.tar.gz.

File metadata

Download URL: coreforecast-0.0.13.1.tar.gz
Upload date: Oct 16, 2024
Size: 87.1 kB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for coreforecast-0.0.13.1.tar.gz
Algorithm	Hash digest
SHA256	`82cfd58e5663e082a44105ea7ad956b8324bb63e1aff224d99a9e2457b8e6914`
MD5	`a4a1d1586572f973d43a5d890e978f3d`
BLAKE2b-256	`6b2e5914f56be5dfe6eebca0136e27f96bfbccac6cd1f1c4e2edf2beaf080209`

Algorithm	Hash digest
SHA256	`6a7bb03c96b93c2d63e383d9d674ba395a5400dd7163696d1fdb981653482e29`
MD5	`ab73290c880c8fcb91cfc4323b80e8f1`
BLAKE2b-256	`85a46e4e58ab3ab274cc8e45b83d3a87115e2f3ecb0fafb98e707f62bfa8f688`

Algorithm	Hash digest
SHA256	`ee93c721a9f79848bc19958c7bae7f7ca10786c4f581626951de3a5b7ea55449`
MD5	`8f5c4fc8329cb28df4304bfcb907aa32`
BLAKE2b-256	`080679924c4ed7edece4e754692c6ee9f4d5e523b5563a79b5a8bbce8b44a1a1`

Algorithm	Hash digest
SHA256	`6b79029e1073f728e6053533f630363318a981457904f887a8bc8af27e03d044`
MD5	`c66a9f41eaba9807c285849a4b25ba46`
BLAKE2b-256	`744285d196a46905a6d03a84ff850237bc4296b238cd7c993eeed441f1b8f899`

Algorithm	Hash digest
SHA256	`7bfa256e8b929081f8a4b715d741336e3188269d22e9a92d040b42196109477f`
MD5	`138d805ea64c2795b79b1b843ed6284b`
BLAKE2b-256	`09acd7c85ad9de7798c6343f2ece79fc793b5602f38e489b45f4a6485f7dc1a7`

Algorithm	Hash digest
SHA256	`5dc93fc7d5fc77e7d7cc572aa8ec970f78f453c659fa6bac6964d245ad70f105`
MD5	`6f38f3bcfa59895017bc8bc258690f5b`
BLAKE2b-256	`55f8318929a46e4258cdf6f7d36baa9d9266c7aeb0d0438c5f02c2927a3ee988`

Algorithm	Hash digest
SHA256	`a3196398dc3ef3126b5e2aa74c5840e576abfa574f2c19d101e178851a57b158`
MD5	`14795a9f13e6c976c9f8acee96640821`
BLAKE2b-256	`feae213fce20fd95fb3058181ec09704100148ae8ccb25baf88a4e7c8ef64b49`

Algorithm	Hash digest
SHA256	`d88cc0ae82dc5fa4fac390b35553ac869e61d06b4f9fe08e45895d3e69153c51`
MD5	`7bd614bd32370404b5a2057613659c17`
BLAKE2b-256	`a8d6ec4620507d7fb79a5e9fea5e4412be979064733d1c10b2d7adcf64b340b9`

Algorithm	Hash digest
SHA256	`9020bd54a3eeeb1ebe486b34c8dda6aa2fdc4287ea0aa75f2ee43219f75ccda4`
MD5	`8cae8f68be7497789a57e3df7506b25a`
BLAKE2b-256	`31b00d9a25938649532986b12627aa49e8a848a3ca3185fd75cfe1715abfdf77`

Algorithm	Hash digest
SHA256	`f4854c7b975d33cbb7d7bde2aae2b16c32512f478dee6202b4bdc0207930f7c9`
MD5	`bba9807ae37472a84edb7283b867ea6d`
BLAKE2b-256	`7446340a77a7d821b16e5ff6b520d94929970366ae7e4788cb4e4d3d8869b779`

Algorithm	Hash digest
SHA256	`64d5831195b6d871ef10eaa78d8e3d2d2944213c8f2edc5049a9e9bd6881c736`
MD5	`a53c67f080c069cc9d89cfc32c374488`
BLAKE2b-256	`d6e2a5bb926a80843ce35e42fd4d022ac1920b412de45f146305e87fce9897ab`

Algorithm	Hash digest
SHA256	`db63411009da7e8b1a6cf9442b7aabefa6c215de9082d893a84e589704f75e64`
MD5	`344667fb24d7191acbf0ce18e886e683`
BLAKE2b-256	`b399141169067078bf54d76b02754cca307fa708a77010c2e0a3802aca1e386e`

Algorithm	Hash digest
SHA256	`2903c3e7c6873e291ca045da586ec44a38712e6bb7397687472b2f3145ef7696`
MD5	`bbbaab4b733cdc42ff497a274e55d0f5`
BLAKE2b-256	`65c80a5cca57b7f7ab0d9ead2ad57f06561ff1e78594fc68d7f69bc29244e79b`

Algorithm	Hash digest
SHA256	`cbdf117c71195ebfc5c2df4f909303ff1179d92d49a75cad5ce43d6a15949eae`
MD5	`5e47b3d1e88c54b28e771d239a0d41fa`
BLAKE2b-256	`f44996c3a5a249acef7ff13a7deaaf83b268abed3a97a8a5eac40f79c6367677`

Algorithm	Hash digest
SHA256	`9546c8a41bcf8a8e80b7d37aeac406eedd7cc9ac3f64bac5966c6f0222a49a64`
MD5	`cc3ee7c2b5c58b57a88408923e559f38`
BLAKE2b-256	`2ac48d6fbe714d8e0aa5f2800e852e1652ed95d0ca37e5ef2bb3808b642e4dbb`

Algorithm	Hash digest
SHA256	`013cac058c12af039e323add9cf8bbb437e6bacde48f11cbb06a295d897b42be`
MD5	`e8d83024c0d2403402da236d3798dc03`
BLAKE2b-256	`072a050123089f7f91def5421521bfa272e6dd8a5bfd0aefa5d37df1ef32528c`

Algorithm	Hash digest
SHA256	`ecf2bdda60e8e52aa27e61356cf9c95354274d4a02c694578b0766b33beb0050`
MD5	`53f4917cdcc0830e0bb86611580967c6`
BLAKE2b-256	`1a145988f0ff94ec2beae8521be1fdaa8dd0a14187f2ae87920d64439d90fd9c`

Algorithm	Hash digest
SHA256	`2b120c8dda5dafa10fdd7db08e9292a4de428ee955d5daba3db346f3b075c4c4`
MD5	`482a11db605c9a64f124d54b1d6dde0a`
BLAKE2b-256	`28ec0c7371d965d6add5ca146bb03d10d9182a1230bd4c42e6fead9398a24f28`

Algorithm	Hash digest
SHA256	`66a32e817ff22cdacabb5485e20cefd4a26e44c0c4aff82ee380577fd76bd536`
MD5	`2cdba38b1b2d4d2d664a65766e04a250`
BLAKE2b-256	`bab271afe2777626a00f7561c37c107c3730cbcd19bd5a64289d89f2737931a7`

Algorithm	Hash digest
SHA256	`42481233236cd70b5aac3ff7efbad3f68cb5817777b99002ed07d9b1e9d06e5c`
MD5	`9f651f4f2c8ad97d5e8902b448d06d21`
BLAKE2b-256	`7939ee94dd673a87f0cb521225e5866398ebca804a1c17ed94b7fcc4537823bc`

Algorithm	Hash digest
SHA256	`4572a84bec17ba341c9c0c3ba05fc26170f876a274b933db4bc06154d728e400`
MD5	`6f40c5737b5f88cd0932e7bb7cf4aca2`
BLAKE2b-256	`af49b65503db194f693b8cbe954d95d30b2e07cd786d723aff37da5fd5804877`

coreforecast 0.0.13.1

Navigation

Verified details

Project links

GitHub Statistics

Maintainers

Unverified details

Meta

Classifiers

Project description

Motivation

Installation

PyPI

conda-forge

Minimal example

Single-array functions

Project details

Verified details

Project links

GitHub Statistics

Maintainers

Unverified details

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

Algorithm	Hash digest
SHA256	`aefb7d6a05008dc37af5f4214f014bed5e8ab6ceba0852db399057a98bba7471`
MD5	`b0b20c4dedce99399c7e78f95eb6bc14`
BLAKE2b-256	`5f1ee28bec35ba56e0b899cc8815a5e2f5a2c0e1d11161a9347bdc95e738bbb4`

Algorithm	Hash digest
SHA256	`1aaa39da298cc6294f3ec83312f061ee0702def399de2bb082df8200a57d8c53`
MD5	`5004f9957a1ea7f0907eb6fd94910fb1`
BLAKE2b-256	`6f3ed60d61d00bdc90055fd93bc11435b399c8e8d5a20692f6a021c5e594c137`

Algorithm	Hash digest
SHA256	`43db9dec4d3d9007523d3d5c7325e9d4386cdef289a58a4fb72a27820a1c9367`
MD5	`4d2e9f150d894eb1823e243002f00974`
BLAKE2b-256	`afd5ae2e9256e3f260f3842c4a58a0bd824450f4ea82b8dfd754ca4b5cb96856`

Algorithm	Hash digest
SHA256	`2804e500c5b83d929698436b02c57a38731a93f332ec084914e53e759a905b65`
MD5	`16fdb6a8d6cfcb44899b326371c971ef`
BLAKE2b-256	`a9c0a1aa6bd4ddeca017295ba95663a2fdee693713e8bdf04d8e5262dfb58942`

Algorithm	Hash digest
SHA256	`cc30c73f6d7cba65a47cb90203645de35c4c35f7eb82eaa495a6353d2f9d1ab8`
MD5	`0b72f57b024acb08230334f7dc962aea`
BLAKE2b-256	`61a7e5be48e1f271dc554ede43f79d89c9f2378f86d0922bfb089985a9be582a`