import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.metrics import r2_score
from sklearn.metrics import median_absolute_error
from scipy.optimize import minimize
import statsmodels.tsa.api as smt
import statsmodels.api as sm
from tqdm import tqdm_notebook
from itertools import product
def mean_absolute_percentage_error(y_true, y_pred):
return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
DATAPATH = 'data/stock_prices_sample.csv'
data = pd.read_csv(DATAPATH, index_col=['DATE'], parse_dates=['DATE'])
data.head(10)
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