Exploratory Data Analysis for Time Series

Duration: 5 min

This module delves into the essential techniques for Exploratory Data Analysis (EDA) specifically tailored for time series data. Understanding the underlying patterns, trends, and seasonality in time series data is crucial for effective forecasting and decision-making.

Understanding Time Series Components

Time series data typically consists of several components: trend, seasonality, and residuals. The trend represents the long-term progression, seasonality captures repeating patterns over fixed periods, and residuals account for the random, unpredictable noise. Identifying these components is the first step in EDA for time series.

import pandas as pd
import matplotlib.pyplot as plt

# Sample time series data
data = {'date': pd.date_range(start='1/1/2020', periods=100),
         'value': [i + 5*np.sin(i/10) + np.random.normal(0, 2) for i in range(100)]}
df = pd.DataFrame(data)
df.set_index('date', inplace=True)

# Plotting the time series
df.plot(figsize=(10, 5))
plt.title('Sample Time Series Data')
plt.show()

Try it in Google Colab:

A line plot showing the sample time series data with a visible trend and some seasonality.

Decomposing Time Series Data

Decomposition is a technique used to separate the time series into its individual components. This helps in understanding the underlying structure of the data and can be crucial for preprocessing before applying forecasting models.

from statsmodels.tsa.seasonal import seasonal_decompose

# Decomposing the time series
decomposition = seasonal_decompose(df['value'], model='additive', period=10)

# Plotting the decomposed components
decomposition.plot()
plt.show()

💡 Tip: Always check the stationarity of your time series data before applying certain models like ARIMA. Non-stationary data can lead to misleading results.

Key Concepts

Check Your Understanding