training run 20260419_211104

README.md

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+---
+license: apache-2.0
+tags:
+  - time-series-forecasting
+  - f1
+  - chronos
+  - fine-tuned
+language:
+  - en
+---
+
+# F1 Lap-Time Forecasting — Chronos Fine-tune
+
+One-step-ahead lap time forecasting for Formula 1 races, fine-tuned from [autogluon/chronos-bolt-base](https://huggingface.co/autogluon/chronos-bolt-base).
+
+| | |
+|---|---|
+| **Run ID** | `20260419_211104` |
+| **Base model** | `autogluon/chronos-bolt-base` |
+| **Task** | Next-lap time regression (ms) within a stint |
+| **Repository** | `pablog86/oaa2` |
+
+---
+
+## Dataset
+
+Sourced from the consolidated `f1-race-data/latest.parquet` Parquet produced by the Airflow ingestion DAG (SPEC-008/SPEC-009).
+
+| | |
+|---|---|
+| **Train / val seasons** | [2022, 2023] |
+| **Test seasons** | [2024] |
+| **Session types** | RACE |
+| **Object ETag** | `a6b0d597d06b934476b5e77a928c9659` |
+| **Last modified** | 2026-04-19T04:22:53+00:00 |
+
+---
+
+## Features
+
+The model receives a sliding context window of pit-corrected lap times (`lap_time_ex_pit_ms`) within the current stint, plus the covariates listed below.
+
+| Feature | Type |
+|---|---|
+| `lap_time_ex_pit_ms` (context window) | Target signal — sliding window of pit-stop-corrected lap times within the current stint |
+| `circuit_id` | Static |
+| `constructor_id` | Static |
+| `grid_position` | Static |
+| `circuit_season_max_laps` | Static |
+| `lap`, `lap_position`, `lap_in_race_pct` | Dynamic observed |
+| `lap_in_stint`, `is_pit_out_lap`, `is_pit_in_lap` | Dynamic observed |
+| `cumulative_pit_count`, `laps_since_last_pit` | Dynamic observed |
+| `elapsed_race_time_ms` | Dynamic observed |
+| `lag_1/2/3_lap_time_ex_pit_ms` | Historical |
+| `rolling_mean_3/5_lap_time_ex_pit_ms` | Historical |
+| `rolling_std_3_lap_time_ex_pit_ms` | Historical |
+| `delta_vs_prev_lap_ex_pit_ms` | Historical |
+| `delta_vs_rolling_mean_3_ex_pit_ms` | Historical |
+
+---
+
+## Training Configuration
+
+| Hyperparameter | Value |
+|---|---|
+| `model_id` | `autogluon/chronos-bolt-base` |
+| `learning_rate` | `0.0001` |
+| `batch_size` | `32` |
+| `max_epochs` | `15` |
+| `patience` | `3` |
+| `weight_decay` | `0.0001` |
+| `gradient_clip_val` | `1.0` |
+| `context_length` | `20` |
+| `prediction_length` | `1` |
+| `loss_function` | `huber` |
+| `random_seed` | `42` |
+| **Early stopping at epoch** | 8 / 15 |
+
+---
+
+## Evaluation Results
+
+### Validation
+
+| Metric | Value |
+|---|---|
+| MAE (ms) | 8377.9 |
+| RMSE (ms) | 80676.4 |
+| MAPE (%) | 8.07 |
+| Within ±500 ms (%) | 55.7 |
+| Within ±1000 ms (%) | 70.7 |
+| Samples | 8873 |
+
+### Test
+
+| Metric | Value |
+|---|---|
+| MAE (ms) | 4619.8 |
+| RMSE (ms) | 55007.5 |
+| MAPE (%) | 4.54 |
+| Within ±500 ms (%) | 60.3 |
+| Within ±1000 ms (%) | 75.6 |
+| Samples | 25241 |
+
+### Test — by season
+
+| Segment | mae (ms) | mape (%) | pct_within_500ms | n_samples |
+| --- | --- | --- | --- | --- |
+| 2024 | 4619.8 | 4.5 | 60.3 | 25241 |
+
+### Test — by stint length
+
+| Segment | mae (ms) | mape (%) | pct_within_500ms | n_samples |
+| --- | --- | --- | --- | --- |
+| long (>30) | 4393.4 | 4.5 | 62.0 | 9235 |
+| medium (15–30) | 3189.5 | 3.3 | 61.8 | 12629 |
+| short (<15) | 10587.5 | 9.2 | 50.3 | 3377 |
+
+---
+
+## Baseline Comparison (test set)
+
+| Metric | **Model** | `last_lap` | `rolling_mean_3` | `rolling_mean_5` |
+| --- | --- | --- | --- | --- |
+| MAE (ms) | **4619.8** | 5167.4 | 3152.9 | 2975.1 |
+| MAPE (%) | **4.5** | 5.6 | 3.5 | 3.4 |
+| Within ±500 ms (%) | **60.3** | 58.7 | 62.4 | 59.5 |
+
+---
+
+## Run History
+
+| run_id | model | val MAE (ms) | test MAE (ms) | test MAPE (%) | within ±500 ms (%) |
+|---|---|---|---|---|---|
+| `20260419_211104` | `chronos-bolt-base` | 8377.9 | 4619.8 | 4.54 | 60.3 |
+
+---
+
+## Reproducibility
+
+| Artifact | Path in this repo |
+|---|---|
+| Dataset manifest | [`dataset_manifest/20260419_211104.json`](dataset_manifest/20260419_211104.json) |
+| Run metadata (metrics + config) | [`runs/20260419_211104.json`](runs/20260419_211104.json) |
+| TensorBoard logs | [`tensorboard/20260419_211104/`](tensorboard/20260419_211104/) |

config.json

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+{
+  "architectures": [
+    "ChronosBoltModelForForecasting"
+  ],
+  "chronos_config": {
+    "context_length": 2048,
+    "input_patch_size": 16,
+    "input_patch_stride": 16,
+    "prediction_length": 64,
+    "quantiles": [
+      0.1,
+      0.2,
+      0.3,
+      0.4,
+      0.5,
+      0.6,
+      0.7,
+      0.8,
+      0.9
+    ],
+    "use_reg_token": true
+  },
+  "chronos_pipeline_class": "ChronosBoltPipeline",
+  "classifier_dropout": 0.0,
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "dtype": "float32",
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 0.05,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "pad_token_id": 0,
+  "reg_token_id": 1,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "vocab_size": 2
+}

dataset_manifest/20260419_211104.json

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+{
+  "minio_bucket": "f1-race-data",
+  "minio_object_key": "latest.parquet",
+  "minio_path": "s3://f1-race-data/latest.parquet",
+  "seasons_train_val": [
+    2022,
+    2023
+  ],
+  "seasons_test": [
+    2024
+  ],
+  "session_types": [
+    "RACE"
+  ],
+  "object_etag": "a6b0d597d06b934476b5e77a928c9659",
+  "object_last_modified": "2026-04-19T04:22:53+00:00",
+  "object_size_bytes": 7005155,
+  "dag_version": "116",
+  "train_val_row_count": 47915,
+  "test_row_count": 26574,
+  "manifest_timestamp": "2026-04-19T21:11:03.337994+00:00"
+}

model.safetensors

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run_history.json

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+    "model_id": "autogluon/chronos-bolt-base",
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+    "test_pct_within_500ms": 60.318529376807575,
+    "test_n_samples": 25241
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+]

runs/20260419_211104.json

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+}

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