Metadata-Version: 2.1
Name: MFLES
Version: 0.2.1
Summary: Gradient boosted time series forecasting.
Home-page: https://github.com/tblume1992/MFLES
Author: Tyler Blume
Keywords: forecasting,time series,seasonality,trend
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: tqdm
Requires-Dist: numba
Requires-Dist: matplotlib

# MFLES
A Specific implementation from ThymeBoost written with the help of Numba.

Here is a quick Introduction:

https://github.com/tblume1992/MFLES/blob/main/examples/MFLES_Intro.ipynb


Here is a quick benchmark vs AutoETS from M4:
![alt text](https://github.com/tblume1992/MFLES/blob/main/static/mfles_benchmark.PNG?raw=true "benchmark")
## Quick Start:
Install via pip
```
pip install MFLES
```

Import MFLES class
```
from MFLES.Forecaster import MFLES
```
Import data
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

df = pd.read_csv(r'https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv')
```
Fit and predict!
```
mfles = MFLES()
fitted = mfles.fit(df['Passengers'].values, seasonal_period=12)
predicted = mfles.predict(12)

plt.plot(np.append(fitted, predicted))
plt.plot(df['Passengers'].values)
plt.show()
```
![alt text](https://github.com/tblume1992/MFLES/blob/main/static/mfles_example.png?raw=true "example")
