MAE

In the field of electricity price forecasting, one of the most widely used metrics to measure the accuracy of point forecasts is the mean absolute error (MAE):

\[\begin{split}\begin{align} \mathrm{MAE} &= \frac{1}{N}\sum_{k=1}^{N}|p_k-\hat{p}_k|,\\ \end{align}\end{split}\]

This metric computes the average absolute error between the predicted prices and the real prices. Predictive models that minimize the MAE lead to predictions of the median of the prices. Despite its popularity, the MAE is not always very informative as absolute errors are hard to compare between different datasets.

epftoolbox.evaluation.MAE

epftoolbox.evaluation.MAE(p_real, p_pred)

Function that computes the mean absolute error (MAE) between two forecasts:

\[\mathrm{MAE} = \frac{1}{N}\sum_{i=1}^N \bigl|p_\mathrm{real}[i]-p_\mathrm{pred}[i]\bigr|\]

p_real and p_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, pandas.Series) – Array/dataframe containing the real prices.
• p_pred (numpy.ndarray, pandas.DataFrame, pandas.Series) – Array/dataframe containing the predicted prices.

Returns:

The mean absolute error (MAE).

Return type:

float

Example

>>> from epftoolbox.evaluation import MAE
>>> 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 MAE of the DNN ensemble for the NP market is 1.667 Let’s test the metric for different conditions

>>> # Evaluating MAE when real price and forecasts are both dataframes
>>> MAE(p_pred=fc_DNN_ensemble, p_real=real_price)
1.6670355192007669
>>>
>>> # Evaluating MAE when real price and forecasts are both numpy arrays
>>> MAE(p_pred=fc_DNN_ensemble.values, p_real=real_price.values)
1.6670355192007669
>>>
>>> # Evaluating MAE when input values are of shape (ndays, n_prices/day)
>>> # instead of shape (nprices, 1)
>>> # Dataframes
>>> MAE(p_pred=fc_DNN_ensemble_2D, p_real=real_price_2D)
1.6670355192007669
>>> # Numpy arrays
>>> MAE(p_pred=fc_DNN_ensemble_2D.values, p_real=real_price_2D.values)
1.6670355192007669
>>>
>>> # Evaluating MAE when input values are of shape (nprices,)
>>> # instead of shape (nprices, 1)
>>> # Pandas Series
>>> MAE(p_pred=fc_DNN_ensemble.loc[:, 'DNN Ensemble'],
...     p_real=real_price.loc[:, 'Price'])
1.6670355192007669
>>> # Numpy arrays
>>> MAE(p_pred=fc_DNN_ensemble.values.squeeze(),
...     p_real=real_price.values.squeeze())
1.6670355192007669