ekanshojha / Time-Series-Analysis

Time Series Analysis of Airline Passenger Data, In this time series forecasting, taking data from kaggle site and applying ARIMA and SARIMAX model to evaluate seasional trends of passenger travelling via airlines.

Time-Series-Analysis

Airline Passenger

#Estimation Time Series Decomposition (ETS) import numpy as np import matplotlib.pyplot as plt from sklearn.neighbors import KNeighborsClassifier from statsmodels.tsa.seasonal import seasonal_decompose import pandas as pd
from pmdarima import auto_arima dataset=pd.read_csv("AirPassengers.csv",index_col='Month',parse_dates=True) result=seasonal_decompose(dataset['#Passengers'],model='multiplicative') result.plot() plt.show() print(dataset) result=auto_arima(dataset['#Passengers'],start_p=1,start_q=1,max_p=3,max_q=3,m=12,start_P=0,seasonal=True,d=None,D=1,trace=True,stepwise=True)

result.summary()

result.plot()

plt.show()

print(dataset)

#SARIMAX from statsmodels.tsa.statespace.sarimax import SARIMAX train=dataset.iloc[:len(dataset)-12] test=dataset.iloc[len(dataset)-12:] model=SARIMAX(train['#Passengers'],order=(0,1,1),seasonal_order=(2,1,1,12)) result=model.fit() result.summary() start=len(train) end=len(train)+len(test)-1 pred=result.predict(start,end,typ='levels').rename('prediction') pred.plot(legend=True) test['#Passengers'].plot(legend=True) plt.show()