#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 13:37:58 2021
@author: sadrachpierre
"""

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.seasonal import seasonal_decompose

df = pd.read_csv("AirPassengers.csv")

print(df.head())

print(df.tail())

df['Month'] = pd.to_datetime(df['Month'], format='%Y-%m')

df.index = df['Month']
del df['Month']
print(df.head())

# sns.lineplot(data=df)
# plt.ylabel("Number of Passengers")
# plt.show()

rolling_mean = df.rolling(7).mean()
rolling_std = df.rolling(7).std()

plt.plot(df, color="blue", label="Original Passenger Data")
plt.plot(rolling_mean, color="red", label="Rolling Mean #Passenger")
plt.plot(rolling_std, color="black", label="Rolling Standard Deviation in #Passenger")
plt.title("Passenger Time Series, Rolling Mean, Standard Deviation")
plt.legend(loc="best")
plt.show()

adft = adfuller(df, autolag="AIC")

output_df = pd.DataFrame({"Values": [adft[0], adft[1], adft[2], adft[3], adft[4]['1%'], adft[4]['5%'], adft[4]['10%']],
                          "Metric": ["Test Statistics", "p-value", "No. of lags used", "Number of observations used",
                                     "critical value (1%)", "critical value (5%)", "critical value (10%)"]})
print(output_df)

autocorrelation_lag1 = df['#Passengers'].autocorr(lag=1)
print("One Month Lag: ", autocorrelation_lag1)

autocorrelation_lag3 = df['#Passengers'].autocorr(lag=3)
print("Three Month Lag: ", autocorrelation_lag3)

autocorrelation_lag6 = df['#Passengers'].autocorr(lag=6)
print("Six Month Lag: ", autocorrelation_lag6)

autocorrelation_lag9 = df['#Passengers'].autocorr(lag=9)
print("Nine Month Lag: ", autocorrelation_lag9)

decompose = seasonal_decompose(df['#Passengers'], model='additive', period=7)
decompose.plot()
plt.show()
df['Date'] = df.index

train = df[df['Date'] < pd.to_datetime("1960-08", format='%Y-%m')]
train['train'] = train['#Passengers']
del train['Date']
del train['#Passengers']
test = df[df['Date'] >= pd.to_datetime("1960-08", format='%Y-%m')]
del test['Date']
test['test'] = test['#Passengers']
del test['#Passengers']
plt.plot(train, color="black")
plt.plot(test, color="red")
plt.title("Train/Test split for Passenger Data")
plt.ylabel("Passenger Number")
plt.xlabel('Year-Month')
sns.set()
plt.show()

from pmdarima.arima import auto_arima

model = auto_arima(train, trace=True, error_action='ignore', suppress_warnings=True)
model.fit(train)
forecast = model.predict(n_periods=len(test))
forecast = pd.DataFrame(forecast, index=test.index, columns=['Prediction'])

plt.plot(train, label='Train')
plt.plot(test, label='Test')
plt.plot(forecast, label='Prediction')
plt.title('#Passenger Prediction')
plt.xlabel('Date')
plt.ylabel('Actual #Passenger')
plt.legend(loc='upper left', fontsize=8)
plt.show()

from math import sqrt
from sklearn.metrics import mean_squared_error

print("RMSE: ", rms)