How to make a Time Series Plot with Rolling Average in Python?

Creating a Time Series Plot with Rolling Average in Python

Time series plots are essential for analyzing data points over a period, revealing trends and patterns. However, raw time series data can be noisy, making it challenging to discern underlying trends. Applying a rolling average helps smooth out short-term fluctuations and highlights longer-term trends. In this guide, we'll demonstrate how to create a time series plot with a rolling average using Python's Pandas and Seaborn libraries.

Understanding Rolling Average

A rolling average (or moving average) computes the average of a fixed number of previous data points. It's particularly useful in time series analysis to smooth out short-term variations and emphasize longer-term trends. In Pandas, the rolling average can be calculated using the rolling() function followed by mean():

data['7day_rolling_avg'] = data['Births'].rolling(window=7).mean()

This code computes the 7-day rolling average of the 'Births' column in the dataset.

Step-by-Step Implementation

Let's walk through the process of creating a time series plot with a rolling average:

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Step 1: Import the dataset
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-total-female-births.csv"
data = pd.read_csv(url)

# Step 2: Compute the rolling average
data['7day_rolling_avg'] = data['Births'].rolling(window=7).mean()

# Step 3: Plot the time series with rolling average
plt.figure(figsize=(12, 5))
sns.lineplot(x='Date', y='Births', data=data, label='Daily Births')
sns.lineplot(x='Date', y='7day_rolling_avg', data=data, label='7-Day Rolling Average')
plt.xlabel('Date')
plt.ylabel('Number of Births')
plt.title('Daily Female Births with 7-Day Rolling Average')
plt.xticks(rotation=45)
plt.legend()
plt.tight_layout()
plt.show()

In this implementation:

• rolling(window=7) defines a rolling window of 7 days.
• mean() calculates the average for each window.
• sns.lineplot() is used to plot the data.

Interpreting the Results

The resulting plot will display two lines:

• Daily Births: The raw daily data points.
• 7-Day Rolling Average: A smoothed line representing the average number of births over the past week.

By comparing these lines, you can observe how the rolling average smooths out short-term fluctuations, making it easier to identify long-term trends and patterns in the data.

Conclusion

Creating a time series plot with a rolling average in Python is straightforward using Pandas and Seaborn. This approach enhances data visualization by smoothing out noise, allowing for clearer insights into underlying trends. Whether you're analyzing stock prices, weather patterns, or any other time-dependent data, applying a rolling average can be a valuable tool in your data analysis toolkit.