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 |
---|---|---|
Benchmark | Naive | 1 |
Exponential Smoothing | SES | 1 |
Exponential Smoothing | Holt's linear | 1 |
Exponential Smoothing | Holt-Winter | 1 |
- | Prophet | |
Neural networks | ANN | |
Neural networks | LSTM | |
Neural networks | TCN |
Documentation
Tutorial notebooks:
Development roadmap
- Memes
- wtorek - czy działa
- FEATURES + SHAP
- x13
- prettier plot
- Alphabet quiz
- Handling many observations per date
- Constant window for forecasting
- For NN - chart of how it learned
- Logging
- Read the docs
- prod
- save picture optional
- PI Coverage
- Watermark
Project details
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