good first issueimprovement
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- Métriques de merge PR
- (Merge moyen 143j 16h) (16 PRs mergées en 30 j)
Description
Hello! Firstly, I wanted to thank you for this wonderful library.
Unified API to get model's params might make darts more convenient.
For example, to get params of Exponential Smoothing model, We have to code like this:
model = ExponentialSmoothing()
model.fit(train)
print(model.model.model.params) # {'smoothing_level': 0.5789473661331209, 'smoothing_slope': ...
So, I want to implement model.get_params() method like sklearn.model.params attribute with property like statsmodels and fbprophet.
I would like to get your opinion before send PR. Thanks!
- Models
- abstract
params()function in theForecastingModelsuperclass - ARIMA
- AutoARIMA
- Baseline Models
- NaiveDrift
- NaiveMean
- NaiveSeasonal
- ExponentialSmoothing
- FFT
- Prophet
- StandardRegressionModel
- TCNModel
- Theta
- TorchForecastingModel (RNNModel)
- abstract
- Preprocessing
- Scaler wrapper