M4-methods provides resources for the M4 forecasting competition, a continuation of previous competitions organized by Spyros Makridakis. This repository contains code implementations of submitted forecasting methods, benchmarks, and evaluation tools. It aims to facilitate reproducibility and analysis of forecasting techniques used in the competition. The primary technology utilized is R, with supplementary Python code for machine learning benchmarks.
The repository includes forecasts from a wide array of methods, including statistical benchmarks and machine learning approaches. Notably, it provides detailed results and rankings of submitted methods across different frequencies and domains. The organized structure facilitates easy access to code, data, and evaluation metrics for replication and comparison.
- Point Forecasts: Contains point forecasts from M4 submissions, benchmarks, and standards.
- Prediction Intervals: Includes prediction intervals for M4 submissions, benchmarks, and standards.
- Benchmarks and Evaluation: Provides R code for generating statistical benchmarks and evaluation metrics.
- ML_benchmarks: Offers Python code for generating machine learning benchmarks.
- 4Theta method: Includes R code for the 4Theta method, a modification of the Theta method.
- Evaluation and Ranks: Presents scores and rankings of methods based on point forecasts and prediction intervals.
- Dataset: Holds the train and test datasets along with information files about the competition series.
The project appears to be a completed archive of submissions and related materials from the M4 forecasting competition. Recent activity ceased in 2020, suggesting the focus is on providing a historical record rather than active development. Documentation is present, but primarily focuses on data and file structures. The project's reliability is tied to the accuracy and completeness of the submitted code and data.
This repository benefits researchers and practitioners interested in time series forecasting techniques and the M4 competition results. It enables comparison of various forecasting methods and provides insights into their performance across different scenarios. Compared to manually implementing these methods, this repository offers a convenient and efficient way to access and reproduce the results of the competition.