Fev

Abstract

1. Introduction

fev (Forecast EValuation library) is a lightweight package that makes it easy to benchmark time series forecasting models.

fev aims for the middle ground - it provides the core benchmarking functionality without introducing unnecessary constraints or bloated dependencies. The library supports point & probabilistic forecasting, different types of covariates, as well as all popular forecasting metrics.

2. Objective

Forecast evaluation library

This project demonstrates how Python can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/autogluon/fev.git
cd fev

1. past_data contains the past data before the forecast horizon (item ID, past timestamps, target, all covariates).
2. future_data contains future data that is known at prediction time (item ID, future timestamps, and known covariates)

pip install fev

import fev

task = fev.Task(
    dataset_path="autogluon/chronos_datasets",
    dataset_config="m4_hourly",
    horizon=24,
)

for window in task.iter_windows():
    past_data, future_data = window.get_input_data()

def naive_forecast(y: list, horizon: int) -> dict[str, list[float]]:
    # Make predictions for a single time series
    return {"predictions": [y[-1] for _ in range(horizon)]}

predictions_per_window = []
for window in task.iter_windows():
    past_data, future_data = window.get_input_data()
    predictions = [
        naive_forecast(ts[task.target_column], task.horizon) for ts in past_data
    ]
    predictions_per_window.append(predictions)

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by autogluon and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.