Time series plotting – basic plots and ACF/PACF plots

Visualizing time series data is a crucial step in understanding its underlying patterns and trends. In this section, we’ll explore various time series plots and how to create them using Python. These visualizations help us gain insights into seasonality, trends, and autocorrelation within the time series data.

We’ll start by loading the required libraries:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

Then, we must load the data from Excel and ensure the date information is converted correctly:

# Load time series data (replace 'time_series_data.xlsx' with your data file)
data = pd.read_excel('time_series_data.xlsx')
# Convert the 'Date' column to datetime format and set it as the index
data['Date'] = pd.to_datetime(data['Date'])
data...