| Name | fold-models JSON |
| Version |
0.1.2
JSON |
| download |
| home_page | None |
| Summary | Models for fold. |
| upload_time | 2023-05-03 08:52:28 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | LICENSE
Copyright (c) 2022 - present Myalo UG (haftungbeschränkt)
(Mark Aron Szulyovszky, Daniel Szemerey) <info@dreamfaster.ai>
Software: fold-models (https://www.github.com/dream-faster/fold-models)
Licensor: Myalo UG (haftungsbeschränkt), Berlin, Germany
PREAMBLE
The goal of this license is to contribute to the advance of Time-Series
and Nowcasting Research by granting access to this software for
non-commercial use. For Commercial use or Commercial Entities
or Public Entities (with the exceptions below)
a license has to be purchased from the Licensor.
TERMS AND CONDITIONS
The Licensor hereby grants non-commercial entities the right
to copy, modify, create derivative works, redistribute, and make non-commercial
use of the Licensed Work. The Licensor may make an Additional Use Grant,
above, permitting commercial use.
Public (Government) and Commercial entities are required to purchase a license or
apply and receive a non-profit exception, unless the use is for physical
or mental health care, family and social services, social welfare,
senior care, child care, and the care of persons with disabilities.
A trial period of thirty (30) days is granted for all, after which
the Licensed Work can only be used if in complicance with the
License Terms and Conditions.
If your use of the Licensed Work does not comply with the requirements
currently in effect as described in this License, you must purchase a
commercial license from the Licensor, its affiliated entities, or
authorized resellers, or you must refrain from using the Licensed Work.
All copies of the original and modified Licensed Work, and derivative
works of the Licensed Work, are subject to this License.
You must conspicuously display this License on each original or modified
copy of the Licensed Work. If you receive the Licensed Work in original
or modified form from a third party, the terms and conditions set forth
in this License apply to your use of that work.
Without limiting other conditions in the License, the grant of rights
under the License will not include, and the License does not grant to you,
the right to Sell the Software.
For purposes of the foregoing, “Sell” means practicing any or all of the
rights granted to you under the License to provide to third parties,
for a fee or other consideration (including without limitation fees for hosting
or support services related to the Software), a product or
service whose value derives, entirely or substantially, from the functionality
of the Software.
DISCLAIMER OF WARRANTY
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
LIMITATION OF LIABILITY
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES. |
| keywords |
financial-machine-learning
forecast
forecasting
machine-learning
models
nowcast
time-series
time-series-classification
time-series-regression
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<p align="center" style="display:flex; width:100%; align-items:center; justify-content:center;">
<a style="margin:2px" href="https://github.com/dream-faster/fold-models/actions/workflows/test-baselines.yaml"><img alt="Baselines Tests" src="https://github.com/dream-faster/fold-models/actions/workflows/test-baselines.yaml/badge.svg"/></a>
<a style="margin:2px" href="https://discord.gg/EKJQgfuBpE"><img alt="Discord Community" src="https://img.shields.io/badge/Discord-%235865F2.svg?logo=discord&logoColor=white"></a>
<a style="margin:2px" href="https://calendly.com/nowcasting/consultation"><img alt="Calendly Booking" src="https://shields.io/badge/-Speak%20with%20us-orange?logo=minutemailer&logoColor=white"></a>
</p>
<!-- PROJECT LOGO -->
<br />
<div align="center">
<a href="https://dream-faster.github.io/fold/">
<img src="https://raw.githubusercontent.com/dream-faster/fold-models/main/docs/images/logo.svg" alt="Logo" width="90" >
</a>
<h3 align="center"><b>FOLD-MODELS</b></h3>
<p align="center">
<b>Extremely Fast Time Series Models.
<br/>To be used with <a href='https://github.com/dream-faster/fold'>Fold.</a> </b><br>
<br/>
<a href="https://dream-faster.github.io/fold-models/"><strong>Explore the docs »</strong></a>
</p>
</div>
<br />
# Available models
Name | Usage
--------------|----------------------------------------
Naive | `from fold_models import Naive`
NaiveSeasonal | `from fold_models import NaiveSeasonal`
MovingAverage | `from fold_models import MovingAverage`
AR | `from fold_models import AR`
ARIMA | `from fold_models import ARIMA`
# Installation
- Prerequisites: `python >= 3.7` and `pip`
- Install from pypi:
```
pip install fold-models
```
- Depending on what model you'd like to wrap, you can either install the library directly or run
```
pip install "fold-models[<your_library_name>]"
```
# Quickstart
You can quickly train your chosen models and get predictions by running:
```python
from fold import ExpandingWindowSplitter, train_evaluate
from fold.utils.dataset import get_preprocessed_dataset
from fold_models import Naive
X, y = get_preprocessed_dataset(
"weather/historical_hourly_la", target_col="temperature", shorten=1000
)
model = Naive()
splitter = ExpandingWindowSplitter(initial_train_window=0.2, step=50)
scorecard, predictions, trained_pipeline = train_evaluate(model, X, y, splitter)
```
## Our Open-core Time Series Toolkit
[](https://github.com/dream-faster/krisi)
[](https://github.com/dream-faster/fold)
[](https://github.com/dream-faster/fold-models)
[](https://github.com/dream-faster/fold-wrappers)
If you want to try them out, we'd love to hear about your use case and help, [please book a free 30-min call with us](https://calendly.com/nowcasting/consultation)!
## Contribution
Join our [Discord](https://discord.gg/EKJQgfuBpE) for live discussion!
Submit an issue or reach out to us on info at dream-faster.ai for any inquiries.
## Licence & Usage
We want to **bring much-needed transparency, speed and rigour** to the process of creating Time Series ML pipelines, while also building a sustainable business, that can support the ecosystem in the long-term.
Fold's licence is inbetween [source-available](https://en.wikipedia.org/wiki/Source-available_software) and a traditional commercial software licence. It requires a paid licence for any commercial use, after the initial, 30 day trial period.
We also want to contribute to open research by giving free access to non-commercial, research use of `fold`.
[Read more](https://dream-faster.github.io/fold/product/license/)
Raw data
{
"_id": null,
"home_page": null,
"name": "fold-models",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": "financial-machine-learning,forecast,forecasting,machine-learning,models,nowcast,time-series,time-series-classification,time-series-regression",
"author": null,
"author_email": "Mark Aron Szulyovszky <mark@dreamfaster.ai>, Daniel Szemerey <daniel@dreamfaster.ai>",
"download_url": "https://files.pythonhosted.org/packages/c3/72/67b898e7017a9fe6ebf3069c718ffec367f54932ef88cdb32f41ac2f00c4/fold_models-0.1.2.tar.gz",
"platform": null,
"description": "<p align=\"center\" style=\"display:flex; width:100%; align-items:center; justify-content:center;\">\n <a style=\"margin:2px\" href=\"https://github.com/dream-faster/fold-models/actions/workflows/test-baselines.yaml\"><img alt=\"Baselines Tests\" src=\"https://github.com/dream-faster/fold-models/actions/workflows/test-baselines.yaml/badge.svg\"/></a>\n <a style=\"margin:2px\" href=\"https://discord.gg/EKJQgfuBpE\"><img alt=\"Discord Community\" src=\"https://img.shields.io/badge/Discord-%235865F2.svg?logo=discord&logoColor=white\"></a>\n <a style=\"margin:2px\" href=\"https://calendly.com/nowcasting/consultation\"><img alt=\"Calendly Booking\" src=\"https://shields.io/badge/-Speak%20with%20us-orange?logo=minutemailer&logoColor=white\"></a>\n</p>\n\n<!-- PROJECT LOGO -->\n\n<br />\n<div align=\"center\">\n <a href=\"https://dream-faster.github.io/fold/\">\n <img src=\"https://raw.githubusercontent.com/dream-faster/fold-models/main/docs/images/logo.svg\" alt=\"Logo\" width=\"90\" >\n </a>\n<h3 align=\"center\"><b>FOLD-MODELS</b></h3>\n <p align=\"center\">\n <b>Extremely Fast Time Series Models.\n <br/>To be used with <a href='https://github.com/dream-faster/fold'>Fold.</a> </b><br>\n <br/>\n <a href=\"https://dream-faster.github.io/fold-models/\"><strong>Explore the docs \u00bb</strong></a>\n </p>\n</div>\n<br />\n\n# Available models\n\nName | Usage\n--------------|----------------------------------------\nNaive | `from fold_models import Naive`\nNaiveSeasonal | `from fold_models import NaiveSeasonal`\nMovingAverage | `from fold_models import MovingAverage`\nAR | `from fold_models import AR`\nARIMA | `from fold_models import ARIMA`\n\n# Installation\n\n- Prerequisites: `python >= 3.7` and `pip`\n\n- Install from pypi:\n ```\n pip install fold-models\n ```\n- Depending on what model you'd like to wrap, you can either install the library directly or run\n ```\n pip install \"fold-models[<your_library_name>]\"\n ```\n\n# Quickstart\n\n\n\n\nYou can quickly train your chosen models and get predictions by running:\n\n```python\n from fold import ExpandingWindowSplitter, train_evaluate\n from fold.utils.dataset import get_preprocessed_dataset\n from fold_models import Naive\n\n X, y = get_preprocessed_dataset(\n \"weather/historical_hourly_la\", target_col=\"temperature\", shorten=1000\n )\n model = Naive()\n splitter = ExpandingWindowSplitter(initial_train_window=0.2, step=50)\n\n scorecard, predictions, trained_pipeline = train_evaluate(model, X, y, splitter)\n```\n\n## Our Open-core Time Series Toolkit\n\n[](https://github.com/dream-faster/krisi)\n[](https://github.com/dream-faster/fold)\n[](https://github.com/dream-faster/fold-models)\n[](https://github.com/dream-faster/fold-wrappers)\n\nIf you want to try them out, we'd love to hear about your use case and help, [please book a free 30-min call with us](https://calendly.com/nowcasting/consultation)!\n\n## Contribution\n\nJoin our [Discord](https://discord.gg/EKJQgfuBpE) for live discussion!\n\nSubmit an issue or reach out to us on info at dream-faster.ai for any inquiries.\n\n\n## Licence & Usage\n\nWe want to **bring much-needed transparency, speed and rigour** to the process of creating Time Series ML pipelines, while also building a sustainable business, that can support the ecosystem in the long-term.\nFold's licence is inbetween [source-available](https://en.wikipedia.org/wiki/Source-available_software) and a traditional commercial software licence. It requires a paid licence for any commercial use, after the initial, 30 day trial period.\n\nWe also want to contribute to open research by giving free access to non-commercial, research use of `fold`. \n\n[Read more](https://dream-faster.github.io/fold/product/license/)\n",
"bugtrack_url": null,
"license": "LICENSE\n \n Copyright (c) 2022 - present Myalo UG (haftungbeschr\u00e4nkt) \n (Mark Aron Szulyovszky, Daniel Szemerey) <info@dreamfaster.ai>\n \n Software: fold-models (https://www.github.com/dream-faster/fold-models)\n Licensor: Myalo UG (haftungsbeschr\u00e4nkt), Berlin, Germany\n \n PREAMBLE\n \n The goal of this license is to contribute to the advance of Time-Series \n and Nowcasting Research by granting access to this software for\n non-commercial use. For Commercial use or Commercial Entities \n or Public Entities (with the exceptions below) \n a license has to be purchased from the Licensor.\n \n \n TERMS AND CONDITIONS\n \n The Licensor hereby grants non-commercial entities the right \n to copy, modify, create derivative works, redistribute, and make non-commercial \n use of the Licensed Work. The Licensor may make an Additional Use Grant, \n above, permitting commercial use. \n \n Public (Government) and Commercial entities are required to purchase a license or \n apply and receive a non-profit exception, unless the use is for physical \n or mental health care, family and social services, social welfare, \n senior care, child care, and the care of persons with disabilities.\n \n A trial period of thirty (30) days is granted for all, after which\n the Licensed Work can only be used if in complicance with the \n License Terms and Conditions.\n \n If your use of the Licensed Work does not comply with the requirements \n currently in effect as described in this License, you must purchase a \n commercial license from the Licensor, its affiliated entities, or \n authorized resellers, or you must refrain from using the Licensed Work.\n \n All copies of the original and modified Licensed Work, and derivative \n works of the Licensed Work, are subject to this License.\n \n You must conspicuously display this License on each original or modified \n copy of the Licensed Work. If you receive the Licensed Work in original \n or modified form from a third party, the terms and conditions set forth \n in this License apply to your use of that work.\n \n Without limiting other conditions in the License, the grant of rights \n under the License will not include, and the License does not grant to you, \n the right to Sell the Software.\n \n For purposes of the foregoing, \u201cSell\u201d means practicing any or all of the \n rights granted to you under the License to provide to third parties, \n for a fee or other consideration (including without limitation fees for hosting\n or support services related to the Software), a product or \n service whose value derives, entirely or substantially, from the functionality \n of the Software. \n \n DISCLAIMER OF WARRANTY\n \n THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY\n APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT\n HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM \"AS IS\" WITHOUT WARRANTY\n OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,\n THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\n PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM\n IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF\n ALL NECESSARY SERVICING, REPAIR OR CORRECTION.\n \n LIMITATION OF LIABILITY\n \n IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING\n WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS\n THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY\n GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE\n USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF\n DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD\n PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),\n EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF\n SUCH DAMAGES.",
"summary": "Models for fold.",
"version": "0.1.2",
"project_urls": {
"Documentation": "https://dream-faster.github.io/fold-models",
"Issues": "https://github.com/dream-faster/fold-models/issues",
"Source": "https://github.com/dream-faster/fold-models"
},
"split_keywords": [
"financial-machine-learning",
"forecast",
"forecasting",
"machine-learning",
"models",
"nowcast",
"time-series",
"time-series-classification",
"time-series-regression"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "fca368d031bcd8c392848c6d80193b3aeccbdb086f63beb5a4847c1391ee0aa0",
"md5": "a4b5ede15b8a6850d39e3a15eb3db0bd",
"sha256": "f28da2e8db7e189c8e44f76bd45d14ad1ca3416f62ae1245d4986c7193dea4c7"
},
"downloads": -1,
"filename": "fold_models-0.1.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "a4b5ede15b8a6850d39e3a15eb3db0bd",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 10018,
"upload_time": "2023-05-03T08:52:26",
"upload_time_iso_8601": "2023-05-03T08:52:26.800247Z",
"url": "https://files.pythonhosted.org/packages/fc/a3/68d031bcd8c392848c6d80193b3aeccbdb086f63beb5a4847c1391ee0aa0/fold_models-0.1.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "c37267b898e7017a9fe6ebf3069c718ffec367f54932ef88cdb32f41ac2f00c4",
"md5": "46d01437c7251995729cc292e053b015",
"sha256": "c97306307a9ca13276352b8fdc0efc77ec4d0e7c3f47687edbb55f20976ba44b"
},
"downloads": -1,
"filename": "fold_models-0.1.2.tar.gz",
"has_sig": false,
"md5_digest": "46d01437c7251995729cc292e053b015",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 8912,
"upload_time": "2023-05-03T08:52:28",
"upload_time_iso_8601": "2023-05-03T08:52:28.674359Z",
"url": "https://files.pythonhosted.org/packages/c3/72/67b898e7017a9fe6ebf3069c718ffec367f54932ef88cdb32f41ac2f00c4/fold_models-0.1.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-05-03 08:52:28",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "dream-faster",
"github_project": "fold-models",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "fold-models"
}
