How to Detect Seasonality in the Time Series Data, And Remove Seasonality in Python

Author(s): Rashida Nasrin Sucky

Originally published on Towards AI.

Detect Seasonality Using Simple Graph, Autocorrelation Function (ACF), and Partial Autocorrelation Function (PACF) in Python
Photo by Balazs Busznyak on Unsplash

Time series data can be subject to seasonal fluctuations. For example, Halloween costumes are supposed to be in high demand during the Halloween season, red roses and candies are around Valentine's Day, and restaurants have more customers during weekends. Seasonality can come in days intervals, week intervals, or months.

It is crucial to understand the seasonality in the time series data so we can produce forecasting models. In this article, I will explain, how to detect the seasonality in the data and how to remove it. I will keep explaining the codes and the process as we move forward.

First necessary import:

import pandas as pdimport matplotlib.pyplot as pltfrom statsmodels.tsa.seasonal import seasonal_decomposefrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf

A dataset of US pollution data will be used for this tutorial. Please feel free to find the dataset in the link below to follow along:

U.S. Pollution Data (kaggle.com)

Reading the dataset into a DataFrame using Pandas read_csv() method.

Then, make the DataFrame smaller by choosing two columns only. I picked the ‘Date Local’ as this is a time series practice and ‘CO Mean’. Please feel free to practice with another variable if you want.

Finally, rename the columns to ‘Date’ and ‘CO’ for convenience.

df… Read the full blog for free on Medium.

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Published via Towards AI