Welcome to sklearn-ts

Testing time series forecasting models made easy :) This package leverages scikit-learn, simply tuning it where needed for time series specific purposes.

Main features include:

• Moving window time split
  • train-test split
  • CV on moving window time splits
• Model wrappers:
  • Neural networks

Other python packages in the time series domain:

• sktime
• sktime-dl
• darts

Installation

pip install sklearn-ts

Quickstart

Forecasting COVID-19 with Linear Regression

from sklearn_ts.datasets.covid import load_covid
from sklearn.linear_model import LinearRegression
from sklearn_ts.validator import check_model

dataset = load_covid()['dataset']
dataset['month'] = dataset['date'].dt.month

params = {'fit_intercept': [False, True]}
regressor = LinearRegression()

results = check_model(
    regressor, params, dataset,
    target='new_cases', features=['month'], categorical_features=[], user_transformers=[],
    h=14, n_splits=2, gap=14,
    plotting=True
)

Forecasting models

Documentation

Tutorial notebooks:

• neural networks

Development roadmap

• usunąć printy
• instrukcja
• prerequisites
• Outliers
• Transform plots
• High level summary
• Handling many observations per date
• Constant window for forecasting
• Image not included
• For NN - chart of how it learned
• Logging
• Read the docs
• CRISP
• SHAP
• prod
• save picture optioal
• not only mae
• single or collective
• memes
• area plot
• feature importances
• watermark
• spójnie - intuicja, wzory
• ARIMA odpowiedniki e Exp