mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-30 14:02:06 +00:00
ee34a1f278
* Fixed isort * Fixed issues * FIxed linters --------- Co-authored-by: James Maslek <jmaslek11@gmail.com>
121 lines
3.8 KiB
Python
121 lines
3.8 KiB
Python
"""Automatic ETS (Error, Trend, Sesonality) View"""
|
|
__docformat__ = "numpy"
|
|
|
|
import logging
|
|
from datetime import datetime
|
|
from typing import List, Optional, Union
|
|
|
|
import matplotlib.pyplot as plt
|
|
import pandas as pd
|
|
|
|
from openbb_terminal.decorators import log_start_end
|
|
from openbb_terminal.forecast import autoets_model, helpers
|
|
|
|
logger = logging.getLogger(__name__)
|
|
# pylint: disable=too-many-arguments
|
|
|
|
|
|
@log_start_end(log=logger)
|
|
def display_autoets_forecast(
|
|
data: Union[pd.DataFrame, pd.Series],
|
|
target_column: str = "close",
|
|
dataset_name: str = "",
|
|
seasonal_periods: int = 7,
|
|
n_predict: int = 5,
|
|
start_window: float = 0.85,
|
|
forecast_horizon: int = 5,
|
|
export: str = "",
|
|
sheet_name: str = None,
|
|
residuals: bool = False,
|
|
forecast_only: bool = False,
|
|
start_date: Optional[datetime] = None,
|
|
end_date: Optional[datetime] = None,
|
|
naive: bool = False,
|
|
export_pred_raw: bool = False,
|
|
external_axes: Optional[List[plt.axes]] = None,
|
|
):
|
|
"""Display Automatic ETS (Error, Trend, Sesonality) Model
|
|
|
|
Parameters
|
|
----------
|
|
data : Union[pd.Series, np.array]
|
|
Data to forecast
|
|
dataset_name str
|
|
The name of the ticker to be predicted
|
|
target_column: Optional[str]:
|
|
Target column to forecast. Defaults to "close".
|
|
seasonal_periods: int
|
|
Number of seasonal periods in a year
|
|
If not set, inferred from frequency of the series.
|
|
n_predict: int
|
|
Number of days to forecast
|
|
start_window: float
|
|
Size of sliding window from start of timeseries and onwards
|
|
forecast_horizon: int
|
|
Number of days to forecast when backtesting and retraining historical
|
|
sheet_name: str
|
|
Optionally specify the name of the sheet the data is exported to.
|
|
export: str
|
|
Format to export data
|
|
residuals: bool
|
|
Whether to show residuals for the model. Defaults to False.
|
|
forecast_only: bool
|
|
Whether to only show dates in the forecasting range. Defaults to False.
|
|
start_date: Optional[datetime]
|
|
The starting date to perform analysis, data before this is trimmed. Defaults to None.
|
|
end_date: Optional[datetime]
|
|
The ending date to perform analysis, data after this is trimmed. Defaults to None.
|
|
naive: bool
|
|
Whether to show the naive baseline. This just assumes the closing price will be the same
|
|
as the previous day's closing price. Defaults to False.
|
|
external_axes:Optional[List[plt.axes]]
|
|
External axes to plot on
|
|
"""
|
|
data = helpers.clean_data(data, start_date, end_date, target_column, None)
|
|
if not helpers.check_data(data, target_column, None):
|
|
return
|
|
|
|
(
|
|
ticker_series,
|
|
historical_fcast,
|
|
predicted_values,
|
|
precision,
|
|
_model,
|
|
) = autoets_model.get_autoets_data(
|
|
data=data,
|
|
target_column=target_column,
|
|
seasonal_periods=seasonal_periods,
|
|
n_predict=n_predict,
|
|
start_window=start_window,
|
|
forecast_horizon=forecast_horizon,
|
|
)
|
|
|
|
if ticker_series == []:
|
|
return
|
|
|
|
probabilistic = False
|
|
helpers.plot_forecast(
|
|
name="AutoETS",
|
|
target_col=target_column,
|
|
historical_fcast=historical_fcast,
|
|
predicted_values=predicted_values,
|
|
ticker_series=ticker_series,
|
|
ticker_name=dataset_name,
|
|
data=data,
|
|
n_predict=n_predict,
|
|
forecast_horizon=forecast_horizon,
|
|
past_covariates=None,
|
|
precision=precision,
|
|
probabilistic=probabilistic,
|
|
export=export,
|
|
sheet_name=sheet_name,
|
|
forecast_only=forecast_only,
|
|
naive=naive,
|
|
export_pred_raw=export_pred_raw,
|
|
external_axes=external_axes,
|
|
)
|
|
if residuals:
|
|
helpers.plot_residuals(
|
|
_model, None, ticker_series, forecast_horizon=forecast_horizon
|
|
)
|