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import argparse
import numpy as np
import pmdarima as pm
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import t

def auto_arima(in_csv_file_path):
	print('IN File==>' + in_csv_file_path)
	df = pd.read_csv(in_csv_file_path, header=0, infer_datetime_format=True, parse_dates=[0], index_col=[0])

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import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

df = pd.read_csv('uciml_auto_city_highway_mpg.csv', header=0)

#Plot the original data set
df.plot.scatter(x='City MPG', y='Highway MPG')
plt.show()

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import matplotlib.pyplot as plt
import pandas as pd
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
import seaborn as sns


df = pd.read_csv('boston_monthly_tmax_1998_2019.csv', header=0, infer_datetime_format=True, parse_dates=[0], index_col=[0])
df.plot(marker='.')
plt.show()

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import pandas as pd
from sklearn import linear_model


#Read the data into a pandas DataFrame
df = pd.read_csv('southern_osc.csv', header=0, infer_datetime_format=True, parse_dates=[0], index_col=[0])

#add two columns containing the LAG=1 and LAG=2 version of the data to the DataFrame
df['T_(i-1)'] = df['T_i'].shift(1)
df['T_(i-2)'] = df['T_i'].shift(2)

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import random
import math

_lambda = 5
_num_arrivals = 100
_arrival_time = 0

print('RAND,INTER_ARRV_T,ARRV_T')

for i in range(_num_arrivals):

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