Time Series Forecasting using ARIMA and SARIMA in Python

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Time series forecasting is a common application in various domains, and ARIMA (AutoRegressive Integrated Moving Average) and SARIMA (Seasonal ARIMA) models are popular tools for this task. In this deep dive, I’ll provide a step-by-step guide on time series forecasting using ARIMA and SARIMA in Python.

1. Import Necessary Libraries:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.stattools import adfuller

2. Load and Explore Time Series Data:

# Load your time series data (replace 'your_data.csv' with your dataset)
df = pd.read_csv('your_data.csv')
df['Date'] = pd.to_datetime(df['Date'])
df.set_index('Date', inplace=True)

3. Visualize Time Series Data:

plt.figure(figsize=(12, 6))
plt.plot(df.index, df['Value'], label='Original Time Series')
plt.title('Time Series Data')
plt.xlabel('Date')
plt.ylabel('Value')
plt.legend()
plt.show()

4. Stationarity Check: