Understanding Time Series

An introduction to time-series data using Python.

What is a time series?

A time series is a sequence of observations collected over time, usually at regular intervals.

Examples include:

• Daily call-center volume
• Monthly sales
• Hourly electricity demand
• Annual unemployment rates

Creating a time series in Python

Let’s create a simple example using pandas.

import pandas as pd
import matplotlib.pyplot as plt

dates = pd.date_range(
    start="2026-01-01",
    periods=30,
    freq="D"
)

volume = pd.Series(
    range(100, 130),
    index=dates,
    name="volume"
)

volume.plot(title="Example Daily Volume")
plt.xlabel("Date")
plt.ylabel("Volume")
plt.show()

This example creates 30 daily observations and plots them over time.

Why does time order matter?

Unlike ordinary independent observations, time-series data can contain trends, seasonality, and serial dependence.

Understanding these patterns helps us select appropriate forecasting methods.