<jupyter_start><jupyter_text>PhDinDS-M5 LeaderboardsThe following table presents the combined leaderboards of the (1) entire M5 competition, (2) entries of the the PhDinDS AIM students, and some (3) M5 benchamarks for comparison.For new entries, you may:1. Add a `.csv` file with the same structure and information in the `time_series_handbook/08_WinningestMethods/M5_competition/leaderboards_data` directory and fill out the necessary information (WRMSSE) per Level in the hierarchy.2. Re-run the code cell below.It is expected that these reported WRMSSE (M5 accuracy metric) for each entry is well-supported by its own `directory` (e.g. `phdinds2024_entry`) that details the methodology used in obtaining the forecasts. Description on the entries are shown in the sections below.Detailed description of the winningest methods, including their code are in the following repository:https://github.com/Mcompetitions/M5-methods<jupyter_code>from glob import glob
import pandas as pd
import os

files = glob('leaderboards_data/*.csv')
leaderboards = pd.concat([pd.read_csv(file) for file in files], axis=0)
display(leaderboards.sort_values(by='AveWRMSSE'))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07269653423499578, 'fraction_numerical': 0.015215553677092139, 'mean_word_length': 6.175757575757576, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '409703', 'license': 'MIT', 'n_tokens_mistral': 343, 'n_tokens_neox': 333, 'n_words': 135, 'path': '08_WinningestMethods/M5_competition/M5_phdinds_leaderboards.ipynb', 'repo_name': 'leolorenzoii/time_series_handbook', 'size': '1183'}