Add TsFile (converted from thuml/Time-Series-Library)

MSL_test.tsfile

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:39bba664c39cca912daa5bd1dc3a2ce2b1ab43741cf7e5a28c08194a7d3a68b0
-size 267385
+oid sha256:f51e94dbcb8db8cff99578b02a52b30254818dc8e3f02a43bb4ba0f30951d023
+size 265182

MSL_train.tsfile

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:937f946e131f22cbcbcb8aaa6731a222f59e583849ae35da3449591bdaa2e9cc
-size 191388
+oid sha256:07977dd22412a2e74453adfebbe645921123ad4e7bff092985335347e20a9395
+size 190658

README.md

@@ -1,56 +1,65 @@
----
-license: cc-by-4.0
-task_categories:
-- time-series-forecasting
-tags:
-- tsfile
-- timeseries
-- time-series
-- anomaly-detection
-pretty_name: MSL (TsFile)
-size_categories:
-- 100K<n<1M
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: MSL_train.tsfile
-  - split: test
-    path: MSL_test.tsfile
----
-
-# MSL (TsFile)
-
-Apache TsFile version of the `MSL` anomaly-detection subset of
-[`thuml/Time-Series-Library`](https://huggingface.co/datasets/thuml/Time-Series-Library).
-
-## Overview
-
-Mars Science Laboratory rover telemetry (55 channels) for anomaly detection.
-
-- **Train:** 58,317 rows (all normal).
-- **Test:** 73,729 rows (with per-timestep 0/1 anomaly labels).
-- **Channels:** 55.
-
-The train and test segments are stored as two separate TsFiles
-(`MSL_train.tsfile` / `MSL_test.tsfile`), preserving the original split.
-
-## Schema (TsFile structure)
-
-- **Time** (INT64, milliseconds) — row index * 1000 ms (the `.npy` source has no timestamp; a monotone 1 Hz proxy axis).
-- **FIELD** (55 channels, FLOAT) — the sensor/metric channels.
-- **label** (INT32) — per-timestep anomaly flag (0/1). The train file is all 0
-  (no ground-truth labels); the test file carries the anomaly labels.
-
-No channels or rows are dropped.
-
-## Usage
-
-Read the `.tsfile` files with the Apache TsFile Java or Python SDK.
-
-## Source & license
-
-- Original dataset: https://huggingface.co/datasets/thuml/Time-Series-Library (subset `MSL`)
-- Author / publisher: thuml (Tsinghua University)
-- Paper: https://arxiv.org/abs/2407.13278
-- License: CC BY 4.0
+---
+license: cc-by-4.0
+task_categories:
+- time-series-forecasting
+tags:
+- tsfile
+- timeseries
+- time-series
+- anomaly-detection
+pretty_name: MSL (TsFile)
+size_categories:
+- 100K<n<1M
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: MSL_train.tsfile
+  - split: test
+    path: MSL_test.tsfile
+  - split: train_labels
+    path: labels/MSL_train_label.csv
+  - split: test_labels
+    path: labels/MSL_test_label.csv
+---
+
+# MSL (TsFile)
+
+Apache TsFile version of the `MSL` anomaly-detection subset of
+[`thuml/Time-Series-Library`](https://huggingface.co/datasets/thuml/Time-Series-Library).
+
+## Overview
+
+Mars Science Laboratory rover telemetry (55 channels) for anomaly detection.
+
+- **Train:** 58,317 rows.
+- **Test:** 73,729 rows.
+- **Channels:** 55.
+
+The train and test segments are stored as two separate TsFiles
+(`MSL_train.tsfile` / `MSL_test.tsfile`), preserving the original split. Labels
+are stored as separate CSV sidecar files in `labels/`.
+
+## Schema (TsFile structure)
+
+- **Time** (INT64, milliseconds) - row index * 1000 ms. The `.npy` source has no
+  timestamp, so this is a monotone 1 Hz proxy axis.
+- **FIELD** (55 channels, FLOAT) - the sensor/metric channels.
+- **Labels** (CSV sidecar) - `labels/MSL_train_label.csv` and
+  `labels/MSL_test_label.csv` contain `Time,label`. The train label file is all
+  0; the test label file carries the per-timestep anomaly labels.
+
+No channels or rows are dropped. Labels are not stored inside the TsFile feature
+tables.
+
+## Usage
+
+Read the `.tsfile` files with the Apache TsFile Java or Python SDK. Join labels
+from the sidecar CSV files by `Time` within the matching split.
+
+## Source & license
+
+- Original dataset: https://huggingface.co/datasets/thuml/Time-Series-Library (subset `MSL`)
+- Author / publisher: thuml (Tsinghua University)
+- Paper: https://arxiv.org/abs/2407.13278
+- License: CC BY 4.0