MAPE¶
Another popular metric for electricity price forecasting is the mean absolute percentage error (MAPE):
This metric computes the MAE between the predicted prices and the real prices and normalizes it by the absolute value of the real prices.
While it provides a relative error metric that would grant comparison between datasets, its values become very large with prices close to zero (regardless of the actual absolute errors) and is also not very informative.
epftoolbox.evaluation.MAPE¶
-
epftoolbox.evaluation.
MAPE
(p_real, p_pred, noNaN=False)[source]¶ Function that computes the mean absolute percentage error (MAPE) between two forecasts:
\[\mathrm{MAPE} = \frac{1}{N}\sum_{i=1}^N \frac{\bigl|p_\mathrm{real}[i]−p_\mathrm{pred}[i]\bigr|}{ \bigl|Y_\mathrm{real}[i]\bigr|}\]p_real
andp_pred
can either be of shape \((n_\mathrm{days}, n_\mathrm{prices/day})\), \((n_\mathrm{prices}, 1)\), or \((n_\mathrm{prices}, )\) where \(n_\mathrm{prices} = n_\mathrm{days} \cdot n_\mathrm{prices/day}\).Parameters: - p_real (numpy.ndarray, pandas.DataFrame) – Array/dataframe containing the real prices.
- p_pred (numpy.ndarray, pandas.DataFrame) – Array/dataframe containing the predicted prices.
- noNaN (bool, optional) – Bool to remove the NaN values resulting of dividing by 0 in the MAPE. It has to be used if any value in p_real is 0.
Returns: The mean absolute percentage error (MAPE).
Return type: float
Example
>>> from epftoolbox.evaluation import MAPE >>> from epftoolbox.data import read_data >>> import pandas as pd >>> >>> # Download available forecast of the NP market available in the library repository >>> # These forecasts accompany the original paper >>> forecast = pd.read_csv('https://raw.githubusercontent.com/jeslago/epftoolbox/master/' + ... 'forecasts/Forecasts_NP_DNN_LEAR_ensembles.csv', index_col=0) >>> >>> # Transforming indices to datetime format >>> forecast.index = pd.to_datetime(forecast.index) >>> >>> # Reading data from the NP market >>> _, df_test = read_data(path='.', dataset='NP', begin_test_date=forecast.index[0], ... end_test_date=forecast.index[-1]) Test datasets: 2016-12-27 00:00:00 - 2018-12-24 23:00:00 >>> >>> # Extracting forecast of DNN ensemble and display >>> fc_DNN_ensemble = forecast.loc[:, ['DNN Ensemble']] >>> >>> # Extracting real price and display >>> real_price = df_test.loc[:, ['Price']] >>> >>> # Building the same datasets with shape (ndays, n_prices/day) instead >>> # of shape (nprices, 1) and display >>> fc_DNN_ensemble_2D = pd.DataFrame(fc_DNN_ensemble.values.reshape(-1, 24), ... index=fc_DNN_ensemble.index[::24], ... columns=['h' + str(hour) for hour in range(24)]) >>> real_price_2D = pd.DataFrame(real_price.values.reshape(-1, 24), ... index=real_price.index[::24], ... columns=['h' + str(hour) for hour in range(24)]) >>> fc_DNN_ensemble_2D.head() h0 h1 h2 ... h21 h22 h23 2016-12-27 24.349676 23.127774 22.208617 ... 27.686771 27.045763 25.724071 2016-12-28 25.453866 24.707317 24.452384 ... 29.424558 28.627130 27.321902 2016-12-29 28.209516 27.715400 27.182692 ... 28.473288 27.926241 27.153401 2016-12-30 28.002935 27.467572 27.028558 ... 29.086532 28.518688 27.738548 2016-12-31 25.732282 24.668331 23.951569 ... 26.965008 26.450995 25.637346
According to the paper, the MAPE of the DNN ensemble for the NP market is 5.38%. Let’s test the metric for different conditions
>>> # Evaluating MAPE when real price and forecasts are both dataframes >>> MAPE(p_pred=fc_DNN_ensemble, p_real=real_price) * 100 5.376051161768693 >>> >>> # Evaluating MAPE when real price and forecasts are both numpy arrays >>> MAPE(p_pred=fc_DNN_ensemble.values, p_real=real_price.values) * 100 5.376051161768693 >>> >>> # Evaluating MAPE when input values are of shape (ndays, n_prices/day) instead >>> # of shape (nprices, 1) >>> # Dataframes >>> MAPE(p_pred=fc_DNN_ensemble_2D, p_real=real_price_2D) * 100 5.376051161768693 >>> # Numpy arrays >>> MAPE(p_pred=fc_DNN_ensemble_2D.values, p_real=real_price_2D.values) * 100 5.376051161768693 >>> >>> # Evaluating MAPE when input values are of shape (nprices,) >>> # instead of shape (nprices, 1) >>> # Pandas Series >>> MAPE(p_pred=fc_DNN_ensemble.loc[:, 'DNN Ensemble'], ... p_real=real_price.loc[:, 'Price']) * 100 5.376051161768693 >>> # Numpy arrays >>> MAPE(p_pred=fc_DNN_ensemble.values.squeeze(), ... p_real=real_price.values.squeeze()) * 100 5.376051161768693