Package for time series forecasting
Project description
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:
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
Model family | Model | Univariate |
---|---|---|
Neural networks | ANN | 1 |
Neural networks | LSTM | 1 |
Neural networks | TCN | 1 |
Documentation
Tutorial notebook preparation in progress.
Development roadmap
- New repo
- Remove old deploy from test
- Pypi
- Exploding MAPE
- Handling many observations per date
- Constant window for forecasting
- Tutorial notebooks
- image not included
Project details
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