Statistics Final Year Topic: Forecast Accuracy of Seasonal Exponential-Smoothing Models under Changing Seasonality

This Statistics final year project uses positive simulated seasonal series with specified trend and amplitude patterns to investigate a specific question in time-series forecasting. The analysis is designed around known generating conditions so that the behaviour of competing statistical procedures can be checked.

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Why choose this project topic?

This study makes seasonal smoothing an explicit, reproducible comparison. Working with positive simulated seasonal series with specified trend and amplitude patterns lets you separate the target quantity from its estimate and explain when a statistical conclusion depends on assumptions.

How do additive and multiplicative seasonal formulations respond when seasonal amplitude changes with the series level?

Agree the scenario ranges, sample sizes and reporting measures for seasonal smoothing before running the study.

Proposed project objectives

01Specify the data-generating conditions for positive simulated seasonal series with specified trend and amplitude patterns.
02Implement a reproducible analysis of seasonal smoothing with documented software settings.
03Evaluate the estimates and uncertainty relevant to this question: How do additive and multiplicative seasonal formulations respond when seasonal amplitude changes with the series level?

A suggested research approach

Generate several controlled seasonal structures, then use identical rolling-origin evaluation for competing models. Compare scale-appropriate errors and inspect behaviour where positivity or seasonal assumptions are violated. Write the analysis before inspecting favourable runs, record random seeds where simulation is used, and keep generated study data distinct from observed field data.

What you will need

A written design for positive simulated seasonal series with specified trend and amplitude patterns
Statistical software supporting time-series forecasting and reproducible scripts
A supervisor-agreed range of scenarios and computational budget

Keep your project scope clear

Multiplicative formulations require suitable data support; forecast comparisons depend on the loss function and evaluation horizon.

Statistics project chapter outline

Use this outline as a starting point. You can edit the chapter titles to match your department’s format during setup.

Chapter 1Introduction
Chapter 2Literature Review
Chapter 3Theory and Methodology
Chapter 4Results and Applications
Chapter 5Summary, Conclusion and Recommendations

Turn this topic into your own final year project.

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