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Time Series Analysis with Python

Common Patterns

Explore the fundamental patterns in time series data such as seasonality and negative autocorrelation. Understand how to use autocorrelograms to identify repeating intervals and opposite movements in data, enhancing your forecasting skills with Python.

We'll cover the following...

Seasonality

The first pattern we'll learn involves identifying seasonality. As we have seen, seasonality is essentially a pattern that repeats in regular intervals of time. Sometimes this pattern will be visible when we plot the line chart, but not always. A good way to spot this is by looking at the autocorrelogram.

Python 3.8
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.graphics.tsaplots import plot_acf
df = pd.read_csv('airpassengers.csv')
# Changing the datatype
df["Month"] = pd.to_datetime(df['Month'], format='%Y-%m')
 
# Setting the Date as index
df = df.set_index('Month')
# Plotting ACF
fig,axe=plt.subplots(figsize=(6,5))
axe.set_xlabel('Lag')
axe.set_ylabel('Correlation')
fig=plot_acf(df['Passengers'],ax=axe, alpha=0.05)
# Display
fig.savefig("output/output.png")
plt.close(fig)

Remember how the airline passengers' data was seasonal? Notice how this autocorrelogram is different than the one from the stock prices' data, with a slight peak around 1212. This means that the correlation between the number of passengers in ...