Ets

Here, we show model performance in predicting the bitcoin price and their hyper-parameters.

Prediction

Prices

DATE	CLOSE
2024-11-11	68,858.94
2024-11-18	68,858.94
2024-11-25	68,858.94
2024-12-02	68,858.94

Charts

Model Performance

For scoring, Root Mean Squared Error was used.

Training data

Mean across all targets: 3,392.11

y_CLOSE_7	y_CLOSE_14	y_CLOSE_21	y_CLOSE_28
52.85	988.64	5,555.41	6,971.54

Test data

Mean across all targets: 5,831.35

y_CLOSE_7	y_CLOSE_14	y_CLOSE_21	y_CLOSE_28
3,857.41	5,131.38	7,727.58	6,609.02

Model Setup

Below are choices made for the model setup.

Exogenous Data

(Determined by Forward Selection and heuristically based on the model's assumptions)

No parameters were selected.

Feature Generator

(Determined by Grid Search)

No parameters were selected.

Feature Scaler

(Determined by heuristically, considering the model's assumptions)

param	description	value
log	Whether to take the log of the data.	True
diff	Whether to difference the data.	False
yeo_johnson	Whether to apply Yeo-Johnson transformation to the data.	False
normalize	Whether to normalize the data.	True

Hyper-parameters

(Determined by Grid Search)

param	description	value
error	The error type of the ETS model.	add
trend	The trend component of the ETS model.	None
seasonal	The seasonal component of the ETS model.	add
damped_trend	Whether the trend is damped in the ETS model.	False
seasonal_periods	The number of seasonal periods in the ETS model.	7