Time Series • Introduction to Time Series

P1.09 Data Sources and Resources

Practical 1 Introduction to Time Series

Quick concept

Compare three resource sets for forecasting next-quarter demand in a small shop, and recommend one that is realistic for a student project.

Practical / Solution

P1.09 Data Sources and Resources

Problem Statement

Compare three resource sets for forecasting next-quarter demand in a small shop, and recommend one that is realistic for a student project.

Learning Outcomes

List forecasting resources.
Match resources to a small practical problem.

Hint

Resources include historical data, methods, software, computing, domain knowledge and skilled people.

Theory

A forecast needs more than a formula. It needs data, a method, tools and people who understand the context.

Dataset / Data Source

No numeric file. Compare: (1) 3 months of notebook totals and a spreadsheet; (2) 5 years of scanned bills with no dates; (3) a claimed 'Kaggle shop dataset' with no verified source.

Analysis / Program

options = [ {"name": "Notebook + spreadsheet", "data": "3 monthly totals", "usable": True}, {"name": "Undated scanned bills", "data": "large but no time index", "usable": False}, {"name": "Unverified download", "data": "unknown origin", "usable": False}, ] for item in options: print(item["name"], "|", item["data"], "| usable:", item["usable"])

Expected Output

Three printed options. Only the dated notebook totals are marked usable.

Result / Interpretation

For a student lab, a small dated series is better than a large undated pile or an unverifiable file. Domain knowledge of the shop still matters.

Note

Do not invent a Kaggle URL. If a public dataset is used later, name it honestly and only when the source is known.