EX.NO.09 -- A project on Time Series Analysis on Weather Forecasting using ARIMA model ...

Date:

AIM :

To Create a project on Time series analysis on weather forecasting using ARIMA model in python and compare with other models.

ALGORITHM :

Step 1 :

Explore the dataset of weather.

Step 2 :

Check for stationarity of time series time series plot :

ACF plot and PACF plot

ADF test

Transform to stationary: differencing

Step 3 :

Determine ARIMA models parameters p, q.

Step 4 :

Fit the ARIMA model.

Step 5 :

Make time series predictions.

Step 6 :

Auto-fit the ARIMA model.

Step 7 :

Evaluate model predictions.

PROGRAM :

Import the neccessary packages :

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.tsa.arima.model import ARIMA
from sklearn.metrics import mean_squared_error

Load the dataset :

data = pd.read_csv("/content/seattle-weather.csv")

Convert 'Date' column to datetime format :

data['date'] = pd.to_datetime(data['date'])

Set 'Date' column as index :

data.set_index('date', inplace=True)

Arima Model :

def arima_model(data, target_variable, order):
    train_size = int(len(data) * 0.8)
    train_data, test_data = data[:train_size], data[train_size:]

    model = ARIMA(train_data[target_variable], order=order)
    fitted_model = model.fit()

    forecast = fitted_model.forecast(steps=len(test_data))

    rmse = np.sqrt(mean_squared_error(test_data[target_variable], forecast))

    plt.figure(figsize=(10, 6))
    plt.plot(train_data.index, train_data[target_variable], label='Training Data')
    plt.plot(test_data.index, test_data[target_variable], label='Testing Data')
    plt.plot(test_data.index, forecast, label='Forecasted Data')
    plt.xlabel('Date')
    plt.ylabel(target_variable)
    plt.title('ARIMA Forecasting for ' + target_variable)
    plt.legend()
    plt.show()

    print("Root Mean Squared Error (RMSE):", rmse)

arima_model(data, 'temp_max', order=(5,1,0))

OUTPUT :

RESULT :

Thus, the program successfully executted based on the ARIMA model using python.

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