manojvenaram/TSA_EXP8

Ex.No: 08 MOVING AVERAGE MODEL AND EXPONENTIAL SMOOTHING

Date: 06.04.2024

AIM:

To implement Moving Average Model and Exponential smoothing Using Python.

ALGORITHM:

1. Import necessary libraries
2. Read the AirLinePassengers data from a CSV file,Display the shape and the first 20 rows of the dataset
3. Set the figure size for plots
4. Suppress warnings
5. Plot the first 50 values of the 'Value' column
6. Perform rolling average transformation with a window size of 5
7. Display the first 10 values of the rolling mean
8. Perform rolling average transformation with a window size of 10
9. Create a new figure for plotting,Plot the original data and fitted value
10. Show the plot
11. Also perform exponential smoothing and plot the graph

PROGRAM:

Import the packages

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.stattools import adfuller
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.ar_model import AutoReg
from sklearn.metrics import mean_squared_error

Read the Airline Passengers dataset from a CSV file

data = pd.read_csv("/content/airline.csv")

Display the shape and the first 50 rows of the dataset

print("Shape of the dataset:", data.shape)
print("First 50 rows of the dataset:")
print(data.head(50))

Plot the first 50 values of the 'International' column

plt.plot(data['International '].head(50))
plt.title('First 50 values of the "International" column')
plt.xlabel('Index')
plt.ylabel('International Passengers')
plt.show()

Perform rolling average transformation with a window size of 5

rolling_mean_5 = data['International '].rolling(window=5).mean()

Display the first 10 values of the rolling mean

print("First 10 values of the rolling mean with window size 5:")
print(rolling_mean_5.head(10))

Perform rolling average transformation with a window size of 10

rolling_mean_10 = data['International '].rolling(window=10).mean()

Plot the original data and fitted value (rolling mean with window size 10)

plt.plot(data['International '], label='Original Data')
plt.plot(rolling_mean_10, label='Rolling Mean (window=10)')
plt.title('Original Data and Fitted Value (Rolling Mean)')
plt.xlabel('Index')
plt.ylabel('International Passengers')
plt.legend()
plt.show()

OUTPUT:

Plot the original data and fitted value

RESULT:

Thus we have successfully implemented the Moving Average Model and Exponential smoothing using python.