Add TsFile (converted from MattiaBonfanti-CS/IN5000-MB-TUD-Forecasting)

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README.md

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+---
+license: mit
+task_categories:
+- time-series-forecasting
+tags:
+- timeseries
+- forecasting
+- tsfile
+- github
+- open-source-software
+---
+
+# IN5000 Open-Source Software Evolution — Forecasting (TsFile)
+
+This dataset is the multivariate time-series forecasting data from the TU Delft
+**IN5000** MSc thesis *"A Framework for Identifying Evolution Patterns of
+Open-Source Software Projects"*, converted from CSV to
+[Apache TsFile](https://tsfile.apache.org/) format.
+
+Each time series describes the evolution of one open-source GitHub repository
+over a sequence of observation steps, across 11 software-engineering activity
+metrics (stars, issues, commits, contributors, deployments, forks, pull
+requests, workflows, releases, repository size, …).
+
+## Source
+
+- **Original dataset**: [MattiaBonfanti-CS/IN5000-MB-TUD-Forecasting](https://huggingface.co/datasets/MattiaBonfanti-CS/IN5000-MB-TUD-Forecasting)
+- **Original code / analysis**: <https://github.com/IN5000-MB-TUD/data-analysis>
+- **Author**: Mattia Bonfanti — MSc Computer Science (Software Technology),
+  TU Delft, IN5000 master's thesis, academic year 2023/2024
+  (m.bonfanti@student.tudelft.nl)
+- **License**: MIT (inherited from the original dataset)
+
+## Data structure
+
+The source is a single CSV (`time_series_data.csv`, 109,882 rows × 14 columns)
+in long format: one row per (repository, step). Key facts derived from the data:
+
+- **1,323 distinct repositories** (`unique_id`, e.g. `nlbdev/pipeline`), each one
+  an independent time series.
+- **`ds`** is a per-series integer step index `0, 1, 2, …` (ordinal observation
+  step, **not** a real calendar timestamp). Series lengths vary (≈30–162 steps,
+  median 83).
+- **`cluster`** ∈ {0, 1, 2} is a fixed evolution-pattern cluster label assigned
+  to each repository (constant within a series).
+- **`repository`** is the integer encoding of `unique_id` (1:1 mapping).
+- 10 integer activity metrics: `stargazers`, `issues`, `commits`,
+  `contributors`, `deployments`, `forks`, `pull_requests`, `workflows`,
+  `releases`, `size`. No missing values.
+
+## TsFile mapping
+
+The TsFile uses the **table model**. One repository = one device (time-series).
+
+| TsFile role | Column(s) | Type | Notes |
+|---|---|---|---|
+| **TAG** (device dimension) | `unique_id`, `cluster` | STRING, INT64 | repository identity + its evolution cluster |
+| **TIME** | derived from `ds` | INT64 (ms) | `ds` value used directly as the millisecond timestamp |
+| **FIELD** | `repository` | INT64 | integer encoding of `unique_id` (redundant, kept on request) |
+| **FIELD** | `stargazers`, `issues`, `commits`, `contributors`, `deployments`, `forks`, `pull_requests`, `workflows`, `releases`, `size` | INT64 | activity metrics |
+
+- Table name: `in5000_oss_forecasting`. Time precision: `ms`.
+- Within each device, rows are sorted by `Time` (ascending).
+
+### Conversion notes (column handling)
+
+- **`ds` → Time**: consumed into the derived `Time` column and **not** kept as a
+  separate field. Lossless: `Time` equals the original `ds`. Because the dataset
+  carries no real calendar dates (only an ordinal step index), the integer step
+  is written directly as a millisecond value.
+- **`repository`** is kept as a FIELD even though it is a 1:1 numeric encoding of
+  the `unique_id` tag (redundant by design choice).
+- **No rows dropped**; the dataset has no train/test split (single CSV).
+
+## Files
+
+```
+.
+├── README.md
+└── data/
+    └── in5000_oss_forecasting.tsfile
+```
+
+## Reading the TsFile
+
+```python
+from tsfile import TsFileReader
+
+reader = TsFileReader("data/in5000_oss_forecasting.tsfile")
+for name, table in reader.get_all_table_schemas().items():
+    print(name, [c.get_column_name() for c in table.get_columns()])
+# Query FIELD/TAG columns of the table 'in5000_oss_forecasting' via reader.query_table(...)
+```
+
+## Citation
+
+Please cite the original thesis / dataset author (Mattia Bonfanti, TU Delft
+IN5000, 2023/2024) and link back to the
+[original dataset](https://huggingface.co/datasets/MattiaBonfanti-CS/IN5000-MB-TUD-Forecasting).

data/in5000_oss_forecasting.tsfile

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