| { |
| "id": "ml20-root", |
| "requirements": "A credible experiment benchmarking classical forecasters (AR, ARIMA, SARIMAX, ETS, Theta, or equivalents) on synthetic seasonal series: model conditions are implemented, execution covers multiple synthetic regimes with repeated seeds, and results address H1/H2/H3 directionally using sMAPE-centered analysis.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative forecaster implementations or substitutes that test the same scientific question should be credited.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml20-code", |
| "requirements": "The forecasting conditions and synthetic data generation pipeline are implemented correctly.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml20-code-methods", |
| "requirements": "The submission implements multiple distinct forecasting methods \u2014 typically AR/ARIMA, a seasonal model (SARIMAX or ETS), and Theta or equivalents \u2014 as separate code paths, and includes at least one explicit naive/seasonal-naive baseline.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml20-code-synthdata", |
| "requirements": "The submission generates multiple synthetic seasonal dataset families with controllable seasonality, trend, and noise, each with a train/test split suitable for forecasting.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| }, |
| { |
| "id": "ml20-code-setup", |
| "requirements": "Forecasting setup uses a fixed holdout horizon (or rolling-origin equivalent) and computes comparable forecasts for every (method, dataset, seed) cell.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Experimental Setup" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml20-exec", |
| "requirements": "Benchmark execution logs required metrics with repeated trials.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml20-exec-metrics", |
| "requirements": "Execution produces a structured metrics artifact containing numeric sMAPE and MAE (or equivalents) for each implemented method on at least one dataset family.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml20-exec-seeds", |
| "requirements": "Each reported method-dataset metric is aggregated over multiple random seeds with dispersion statistics. Honest small-seed runs with variance reported are preferable to a single run.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml20-exec-runtime", |
| "requirements": "The run logs per-method wall-clock timing and completes within a CPU-only budget.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml20-results", |
| "requirements": "Results analysis addresses H1/H2/H3 directionally with quantitative comparisons.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml20-result-h1", |
| "requirements": "The submission isolates high-seasonality low-noise results and conveys whether SARIMAX or ETS tends to attain the best mean sMAPE \u2014 judge directionally against H1.", |
| "weight": 20.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml20-result-h2", |
| "requirements": "The submission compares the best advanced method against seasonal-naive under high-noise settings and conveys whether the sMAPE gap collapses to a small margin (H2).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml20-result-h3", |
| "requirements": "The submission ranks methods by mean sMAPE per dataset family and conveys whether no single method dominates across all families (H3).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml20-result-writeup", |
| "requirements": "The README or writeup reports key metric tables, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations (synthetic realism, grid-search scope, seed count, horizon sensitivity). No strict word-count requirement.", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| } |
|
|
