expo metrics

openbb_terminal/forecast/expo_model.py

@@ -8,7 +8,7 @@ from typing import List, Optional, Tuple, Union
 
 import pandas as pd
 from darts import TimeSeries
-from darts.metrics import mape
+from darts.metrics import mape, rmse, mse
 from darts.models import ExponentialSmoothing
 from darts.utils.utils import ModelMode, SeasonalityMode
 from numpy import ndarray
@@ -40,6 +40,7 @@ def get_expo_data(
     n_predict: int = 5,
     start_window: float = 0.85,
     forecast_horizon: int = 5,
+    metric: str = "mape",
 ) -> Tuple[
     List[TimeSeries],
     List[TimeSeries],
@@ -76,6 +77,8 @@ def get_expo_data(
         Size of sliding window from start of timeseries and onwards
     forecast_horizon: int
         Number of days to forecast when backtesting and retraining historical
+    metric: str
+        Metric to use for backtesting. Defaults to MAPE.
 
     Returns
     -------
@@ -149,8 +152,17 @@ def get_expo_data(
     # we have the historical fcast, now lets train on entire set and predict.
     best_model.fit(ticker_series)
     probabilistic_forecast = best_model.predict(int(n_predict), num_samples=500)
-    precision = mape(actual_series=ticker_series, pred_series=historical_fcast_es)
-    console.print(f"Exponential smoothing obtains MAPE: {precision:.2f}% \n")
+
+    if metric == "rmse":
+        precision = rmse(actual_series=ticker_series, pred_series=historical_fcast_es)
+    elif metric == "mse":
+        precision = mse(actual_series=ticker_series, pred_series=historical_fcast_es)
+    elif metric == "mape":
+        precision = mape(actual_series=ticker_series, pred_series=historical_fcast_es)
+
+    console.print(
+        f"Exponential smoothing obtains {metric.upper()}: {precision:.2f}% \n"
+    )
 
     return (
         ticker_series,

openbb_terminal/forecast/expo_view.py

@@ -36,6 +36,7 @@ def display_expo_forecast(
     end_date: Optional[datetime] = None,
     naive: bool = False,
     export_pred_raw: bool = False,
+    metric: str = "mape",
     external_axes: Optional[List[plt.axes]] = None,
 ):
     """Display Probabilistic Exponential Smoothing forecast
@@ -80,6 +81,10 @@ def display_expo_forecast(
     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.
+    export_pred_raw: bool
+        Whether to export the raw predicted values. Defaults to False.
+    metric: str
+        The metric to use when backtesting. Defaults to "mape".
     external_axes: Optional[List[plt.axes]]
         External axes to plot on
     """
@@ -103,6 +108,7 @@ def display_expo_forecast(
         n_predict=n_predict,
         start_window=start_window,
         forecast_horizon=forecast_horizon,
+        metric=metric,
     )
 
     if ticker_series == []:
@@ -127,6 +133,7 @@ def display_expo_forecast(
         forecast_only=forecast_only,
         naive=naive,
         export_pred_raw=export_pred_raw,
+        metric=metric,
         external_axes=external_axes,
     )
     if residuals: