https://github.com/tblume1992/MFLES

https://github.com/tblume1992/MFLES

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• Host: GitHub
• URL: https://github.com/tblume1992/MFLES
• Owner: tblume1992
• License: mit
• Created: 2023-11-19T22:34:55.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-02-18T15:16:30.000Z (9 months ago)
• Last Synced: 2024-04-29T22:03:43.941Z (6 months ago)
• Language: Jupyter Notebook
• Size: 830 KB
• Stars: 103
• Watchers: 1
• Forks: 4
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Awesome Lists containing this project

• awesome-conformal-prediction - MFLES - Gradient Boosted Decomposition for time series forecasting

README

        
# MFLES v0.2.2

 ![alt text](https://github.com/tblume1992/MFLES/blob/main/static/mfles_logo.png?raw=true "logo")

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

Here is a quick Introduction and demonstration of methods such as Conformal Prediction Intervals and seasonality decomposition:

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")

### Or Optimize

```

mfles = MFLES()

opt_params = mfles.optimize(df['Passengers'].values,

                          seasonal_period=12,

                          test_size=6,

                          n_steps=3, #number of train/test splits to make

                          step_size=6, #the number of periods to move each step

                          metric='mse' #should support smape, mse, mae, mape

                          )

fitted = mfles.fit(df['Passengers'].values, **opt_params)

predicted = mfles.predict(12)

plt.plot(np.append(fitted, predicted))

plt.plot(df['Passengers'].values)

plt.show()

```

### Fitting from dataframe:

```

from MFLES.Forecaster import fit_from_df

output = fit_from_df(df,

                      forecast_horizon=24,

                      freq='M',

                      seasonal_period=12,

                      id_column='unique_id',

                      time_column='ds',

                      value_column='y',

                      floor=0)

```

### Optimizing from dataframe

```

from MFLES.Forecaster import optimize_from_df

output = optimize_from_df(df,

                          forecast_horizon=4,

                          test_size=4,

                          n_steps=3,

                          step_size=1,

                          metric='mse',

                          seasonal_period=12,

                          freq='M')

```

## Gradient Boosted Time Series Decomposition Theory

The idea is pretty simple, take a process like decomposition and view it as

a type of 'psuedo' gradient boosting since we are passing residuals around

simlar to standard gradient boosting. Then apply gradient boosting approaches

such as iterating with a global mechanism to control the process and introduce

learning rates for each of the components in the process such as trend or

seasonality or exogenous. By doing this we graduate from this 'psuedo' approach

to full blown gradient boosting.

This process allows us to fit pretty exotic models and optimize for each learning

rate to make them jive. Also enables online learning since the framework is made

for residuals. Also opens up changepoint detection using segmentation schemes

although that is out-of-scope of this library.

## Citing

```

@software{

author = {Blume Tyler},

license = {MIT License},

title = {{MFLES}},

url = {https://github.com/tblume1992/MFLES},

version = {0.2.2},

year = {2024}

}

```