Metadata-Version: 2.1
Name: TeremokTSLib
Version: 1.1.3
Summary: Easy-to-use box ML solution for forcasting consumption
Home-page: https://teremok.ru/
Author: Alexander Nikitin
Author-email: sniknickitin@yandex.ru
License: MIT
Keywords: forcasting
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
License-File: LICENCE.txt

![Icon](./assets/github_icon.png)

# What is this Library about?
Easy-to-use (4 lines of code, actually) framework for training powerful predictive models!

# Description
We made a mother-model, which consists of multiple layers of predictive models: ewma is used as trend, Prophet is used for getting seasonality, CatBoost is used for predicting residuals.
Why did we do that? Because we needed a out-of-the-box solution, which could be used by non-ML users. 

# How-to-install?
You can install this framework via pypi: 
```
pip install TeremokTSLib
```

# How-to-use?
You can watch an example in TeremokTSLib/tests foulder. All you need is dataframe with 2 columns: date, consumption.
Then you can initiate mother-model and train it with just 2 rows of code:
```
import TeremokTSLib as tts
model = tts.Model()
model.train(data=data)
```

# Maintained by
Library is developed and being maintained by Teremok ML team

# Contacts
- Our website: https://teremok.ru/
- ML team: you can contact us via telegram channel @pivo_txt

Change Log
==========

0.1.0 (27.07.2024)
------------------
- First release

1.1.0 (28.07.2024)
------------------
- Beta verison release
- Visualisation of itertest added

1.1.1 (06.08.2024)
------------------
- Fixed some bugs

1.1.2 (09.08.2024)
------------------
- Added parallel training for Prophet

1.1.3 (16.08.2024)
------------------
- Now predict_order method returns dict with predicted orders and cons
- Added visualisation of optuna trials

