Add TsFile (converted from kashif/App_Flow)

.gitattributes

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+app_flow.tsfile filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,61 @@
+---
+license: apache-2.0
+task_categories:
+- time-series-forecasting
+tags:
+- tsfile
+- format:tsfile
+- timeseries
+modality: timeseries
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: app_flow.tsfile
+---
+
+# App Flow
+
+This dataset consists of hourly maximum traffic flow for 128 systems deployed on 16 logic data centers, resulting in 1083 different time series in total.
+The length of each series is more than 4 months. Each time series is divided into two segments for training and testing with a ratio of 32:1.
+This dataset was collected at Ant Group and does not contain any Personal Identifiable Information and is desensitized and encrypted.
+
+## TsFile Conversion
+
+- Original dataset: [`kashif/App_Flow`](https://huggingface.co/datasets/kashif/App_Flow)
+- Modalities: Time-series
+- Converted data files are listed in the YAML metadata above.
+- Source README text and dataset-specific metadata are retained; the source
+  Usage section is replaced with the executable TsFile Python SDK example below.
+
+- The encrypted source CSV is decoded as provided; `app_name` and `zone` are TAG columns, `time` becomes millisecond `Time`, and only the pandas export index is removed.
+- Rows are stably sorted by `app_name`, `zone`, and `Time`; duplicate or otherwise too-close timestamps within one device receive the smallest deterministic millisecond offsets needed for a strictly increasing TsFile timeline.
+
+## Usage
+
+Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:
+
+```python
+from pathlib import Path
+from tsfile import TsFileReader
+
+path = Path("app_flow.tsfile")
+with TsFileReader(str(path)) as reader:
+    schemas = reader.get_all_table_schemas()
+    print("tables:", list(schemas))
+    table_name = next(iter(schemas))
+    table = schemas[table_name]
+    columns = [column.get_column_name() for column in table.get_columns()]
+    print("columns:", columns)
+    field_names = [
+        column.get_column_name()
+        for column in table.get_columns()
+        if column.get_column_name() not in {"Time", "time"}
+    ]
+    if field_names:
+        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
+            batch = result.read_arrow_batch()
+            if batch is not None:
+                print(batch.to_pandas().head())
+```
+

app_flow.tsfile

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ea656d9e523f9d1920165d00730334360411da1e0fd0273829c66edf08ed440
+size 3584864