mastfm 0.0.4

A package for Meta-learning and data Augmentation for Stress Testing Forecasting Models

mastfm

mastfm is a package for Meta-learning and data Augmentation for Stress Testing. It provides tools to interpret forecasting models by leveraging meta-learning techniques and various data augmentation methods.

Installation

You can install the mastfm package using pip:

pip install mastfm

Requirements

• numpy
• pandas
• scikit-learn
• shap
• shapiq
• matplotlib
• imbalanced-learn
• tsfeatures
• xgboost
• lightgbm
• tqdm
• mlforecast

Usage

Here is a simple example of how to use the mastfm package:

import pandas as pd
from mastfm import MAST
from xgboost import XGBRegressor as xgb

# Load your data into a pandas DataFrame
df = pd.read_csv('your_dataset.csv')

# Initialize the MAST class
mast = MASTFM(
    forecasting_model=xgb(),
    seasonality=12,
    frequency="M",
    horizon=12,
    level=80,
    quantile=80,
    augmentation_method="ADASYN",
)
mast.fit(df=df, target_differences=1)
mast.plot_stress(save=True)
mast.explanations(save=True)

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Ricardo Inácio