Time Series • Introduction to Time Series

P1.02 Classify Types of Data

Practical 1 Introduction to Time Series

Quick concept

Given three small tables, classify each as time series, cross-sectional, or panel data, and justify the classification.

Practical / Solution

P1.02 Classify Types of Data

Problem Statement

Given three small tables, classify each as time series, cross-sectional, or panel data, and justify the classification.

Learning Outcomes

Distinguish time series, cross-sectional and panel data.
Explain who is observed and when they are observed.

Hint

Ask two questions: How many entities? At how many time points?

Theory

Time series data follows one process over time. Cross-sectional data observes many entities at one time. Panel data observes many entities repeatedly over time.

Dataset / Data Source

Table A: daily temperature of one city for 10 days. Table B: income of 8 households in 2026 only. Table C: annual sales of 4 shops from 2023 to 2025. All three tables are teaching examples.

Analysis / Program

# Classify teaching tables by entity and time tables = { "A": {"entities": 1, "times": 10, "example": "daily temperature of one city"}, "B": {"entities": 8, "times": 1, "example": "household income in 2026"}, "C": {"entities": 4, "times": 3, "example": "shop sales over three years"} } for name, info in tables.items(): if info["entities"] == 1 and info["times"] > 1: kind = "Time series" elif info["entities"] > 1 and info["times"] == 1: kind = "Cross-sectional" else: kind = "Panel" print(name, kind, "-", info["example"])

Expected Output

Printed labels: A Time series, B Cross-sectional, C Panel, each with its short example.

Result / Interpretation

Only Table A is a single time series. Table B cannot show change over time. Table C can show both shop differences and year-to-year change.

Note

Forecasting a single variable over time uses time series data. Comparing many units at one moment uses cross-sectional data.