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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import statsmodels.api as sm
from sklearn.preprocessing import MinMaxScaler
from sklearn import metrics
from sklearn.model_selection import train_test_split 
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.neighbors import KNeighborsRegressor
from xgboost import XGBRegressor
from datetime import datetime
import numpy as np             #for numerical computations like log,exp,sqrt etc
import pandas as pd            #for reading & storing data, pre-processing
import matplotlib.pylab as plt #for visualization
#for making sure matplotlib plots are generated in Jupyter notebook itself
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.stattools import acf, pacf
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.arima_model import ARIMA
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 10, 6
import numpy as np      # To use np.arrays
import pandas as pd     # To use dataframes
from pandas.plotting import autocorrelation_plot as auto_corr
# To plot
import matplotlib.pyplot as plt  
import matplotlib as mpl
import seaborn as sns
#For date-time
import math
from datetime import datetime
from datetime import timedelta
# Another imports if needs
import itertools
import statsmodels.api as sm
import statsmodels.tsa.api as smt
import statsmodels.formula.api as smf
from sklearn.model_selection import train_test_split
from statsmodels.tsa.seasonal import seasonal_decompose as season
from sklearn.metrics import mean_squared_error, mean_absolute_error
from sklearn.metrics import accuracy_score, balanced_accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.ensemble import RandomForestRegressor
from sklearn import metrics
from sklearn.linear_model import LinearRegression 
from sklearn import preprocessing
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from statsmodels.tsa.stattools import adfuller, acf, pacf
from statsmodels.tsa.arima_model import ARIMA
!pip install pmdarima
from pmdarima.utils import decomposed_plot
from pmdarima.arima import decompose
from pmdarima import auto_arima