forecast_1task

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error

data = pd.read_csv('/datasets/energy_consumption.csv', index_col=[0], parse_dates=[0])
data.sort_index(inplace=True)
data = data.resample('1D').sum()

train, test = train_test_split(data, shuffle=False, test_size=0.2)


print("Средний объём электропотребления в день:", test['PJME_MW'].mean())

pred_median = np.ones(test.shape) * train['PJME_MW'].median()

my_pred_median = pd.Series(train['PJME_MW'].median(), index=test.index)

mae = mean_absolute_error(test, pred_median)
print("MAE:", mae)