| Name | nixtlats JSON |
| Version |
0.3.0
JSON |
| download |
| home_page | https://github.com/Nixtla/nixtla |
| Summary | Python SDK for Nixtla API (TimeGPT) |
| upload_time | 2024-04-03 02:32:17 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Nixtla [](https://twitter.com/intent/tweet?text=Statistical%20Forecasting%20Algorithms%20by%20Nixtla%20&url=https://github.com/Nixtla/statsforecast&via=nixtlainc&hashtags=StatisticalModels,TimeSeries,Forecasting) [](https://join.slack.com/t/nixtlacommunity/shared_invite/zt-1pmhan9j5-F54XR20edHk0UtYAPcW4KQ)
<div align="center">
<img src="https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/imgs_indx/logo_mid.png">
<h1 align="center">NixtlaTS</h1>
<h3 align="center">Forecast using TimeGPT</h3>
[](https://github.com/Nixtla/nixtla/actions/workflows/ci.yaml)
[](https://pypi.org/project/nixtlats/)
[](https://pypi.org/project/nixtlats/)
[](https://github.com/Nixtla/nixtlats/blob/main/LICENSE)
[](https://nixtla.github.io/nixtla/)
[](https://pepy.tech/project/nixtlats)
**NixtlaTS** offers a collection of classes and methods to interact with the API of TimeGPT.
</div>
# 🕰️ TimeGPT: Revolutionizing Time-Series Analysis
Developed by Nixtla, TimeGPT is a cutting-edge generative pre-trained transformer model dedicated to prediction tasks. 🚀 By leveraging the most extensive dataset ever – financial, weather, energy, and sales data – TimeGPT brings unparalleled time-series analysis right to your terminal! 👩💻👨💻
In seconds, TimeGPT can discern complex patterns and predict future data points, transforming the landscape of data science and predictive analytics.
## ⚙️ Fine-Tuning: For Precision Prediction
In addition to its core capabilities, TimeGPT supports fine-tuning, enhancing its specialization for specific prediction tasks. 🎯 This feature is like training a machine learning model on a targeted data subset to improve its task-specific performance, making TimeGPT an even more versatile tool for your predictive needs.
## 🔄 `NixtlaTS`: Your Gateway to TimeGPT
With `NixtlaTS`, you can easily interact with TimeGPT through simple API calls, making the power of TimeGPT readily accessible in your projects.
## 💻 Installation
Get `NixtlaTS` up and running with a simple pip command:
```python
pip install nixtlats>=0.1.0
```
## 🎈 Quick Start
Get started with TimeGPT now:
```python
df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short.csv')
from nixtlats import NixtlaClient
nixtla = NixtlaClient(
# defaults to os.environ.get("NIXTLA_API_KEY")
api_key = 'my_api_key_provided_by_nixtla'
)
fcst_df = nixtla.forecast(df, h=24, level=[80, 90])
```
Raw data
{
"_id": null,
"home_page": "https://github.com/Nixtla/nixtla",
"name": "nixtlats",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": null,
"author": null,
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/19/a1/aba4ece33c882bc74c71f49ec45e13beba5f75b6bf7868cc3f0256496a82/nixtlats-0.3.0.tar.gz",
"platform": null,
"description": "# Nixtla [](https://twitter.com/intent/tweet?text=Statistical%20Forecasting%20Algorithms%20by%20Nixtla%20&url=https://github.com/Nixtla/statsforecast&via=nixtlainc&hashtags=StatisticalModels,TimeSeries,Forecasting) [](https://join.slack.com/t/nixtlacommunity/shared_invite/zt-1pmhan9j5-F54XR20edHk0UtYAPcW4KQ)\n\n<div align=\"center\">\n<img src=\"https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/imgs_indx/logo_mid.png\">\n<h1 align=\"center\">NixtlaTS</h1>\n<h3 align=\"center\">Forecast using TimeGPT</h3>\n \n[](https://github.com/Nixtla/nixtla/actions/workflows/ci.yaml)\n[](https://pypi.org/project/nixtlats/)\n[](https://pypi.org/project/nixtlats/)\n[](https://github.com/Nixtla/nixtlats/blob/main/LICENSE)\n[](https://nixtla.github.io/nixtla/)\n[](https://pepy.tech/project/nixtlats)\n \n**NixtlaTS** offers a collection of classes and methods to interact with the API of TimeGPT.\n</div>\n\n# \ud83d\udd70\ufe0f TimeGPT: Revolutionizing Time-Series Analysis\n\nDeveloped by Nixtla, TimeGPT is a cutting-edge generative pre-trained transformer model dedicated to prediction tasks. \ud83d\ude80 By leveraging the most extensive dataset ever \u2013 financial, weather, energy, and sales data \u2013 TimeGPT brings unparalleled time-series analysis right to your terminal! \ud83d\udc69\u200d\ud83d\udcbb\ud83d\udc68\u200d\ud83d\udcbb\n\nIn seconds, TimeGPT can discern complex patterns and predict future data points, transforming the landscape of data science and predictive analytics.\n\n## \u2699\ufe0f Fine-Tuning: For Precision Prediction\n\nIn addition to its core capabilities, TimeGPT supports fine-tuning, enhancing its specialization for specific prediction tasks. \ud83c\udfaf This feature is like training a machine learning model on a targeted data subset to improve its task-specific performance, making TimeGPT an even more versatile tool for your predictive needs.\n\n## \ud83d\udd04 `NixtlaTS`: Your Gateway to TimeGPT\n\nWith `NixtlaTS`, you can easily interact with TimeGPT through simple API calls, making the power of TimeGPT readily accessible in your projects.\n\n## \ud83d\udcbb Installation\n\nGet `NixtlaTS` up and running with a simple pip command:\n\n```python\npip install nixtlats>=0.1.0\n```\n\n## \ud83c\udf88 Quick Start\n\nGet started with TimeGPT now:\n\n```python\ndf = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short.csv')\n\nfrom nixtlats import NixtlaClient\nnixtla = NixtlaClient(\n # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n api_key = 'my_api_key_provided_by_nixtla'\n)\nfcst_df = nixtla.forecast(df, h=24, level=[80, 90])\n```\n\n\n",
"bugtrack_url": null,
"license": null,
"summary": "Python SDK for Nixtla API (TimeGPT)",
"version": "0.3.0",
"project_urls": {
"Homepage": "https://github.com/Nixtla/nixtla"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "716962ebf1bb52054029462de847f2c2c545194cf82ac653179a58ae88a19e32",
"md5": "2a2f5f79613136636a62a2a2fc724d99",
"sha256": "6e1daa35c31dc3b871733f8a7f80f6e81870587f694267ec7278c9f080078cf9"
},
"downloads": -1,
"filename": "nixtlats-0.3.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "2a2f5f79613136636a62a2a2fc724d99",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 50834,
"upload_time": "2024-04-03T02:32:15",
"upload_time_iso_8601": "2024-04-03T02:32:15.707898Z",
"url": "https://files.pythonhosted.org/packages/71/69/62ebf1bb52054029462de847f2c2c545194cf82ac653179a58ae88a19e32/nixtlats-0.3.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "19a1aba4ece33c882bc74c71f49ec45e13beba5f75b6bf7868cc3f0256496a82",
"md5": "d77409d8f8a601f57fc262c27060df9a",
"sha256": "ea6868c2891b07fa09b0dd48b204b6ba62fa51aea8117991842ce5ffc330384c"
},
"downloads": -1,
"filename": "nixtlats-0.3.0.tar.gz",
"has_sig": false,
"md5_digest": "d77409d8f8a601f57fc262c27060df9a",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 33480,
"upload_time": "2024-04-03T02:32:17",
"upload_time_iso_8601": "2024-04-03T02:32:17.598728Z",
"url": "https://files.pythonhosted.org/packages/19/a1/aba4ece33c882bc74c71f49ec45e13beba5f75b6bf7868cc3f0256496a82/nixtlats-0.3.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-04-03 02:32:17",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "Nixtla",
"github_project": "nixtla",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "nixtlats"
}
