Datasets:
Tasks:
Time Series Forecasting
Modalities:
Time-series
Size:
10K<n<100K
ArXiv:
Tags:
tsfile
timeseries
modality:timeseries
format:tsfile
multimodal-time-series
context-guided-time-series-forecasting
License:
| license: apache-2.0 | |
| task_categories: | |
| - time-series-forecasting | |
| size_categories: | |
| - 10K<n<100K | |
| tags: | |
| - tsfile | |
| - timeseries | |
| - modality:timeseries | |
| - format:tsfile | |
| - multimodal-time-series | |
| - context-guided-time-series-forecasting | |
| - energy | |
| - traffic | |
| modality: timeseries | |
| pretty_name: CGTSF Context-Guided Time Series Forecasting (TsFile format) | |
| configs: | |
| - config_name: leu | |
| data_files: | |
| - split: train | |
| path: "cgtsf_leu.tsfile" | |
| - config_name: mspg | |
| data_files: | |
| - split: train | |
| path: "cgtsf_mspg.tsfile" | |
| - config_name: ptf | |
| data_files: | |
| - split: train | |
| path: "cgtsf_ptf.tsfile" | |
| # CGTSF: Context-Guided Time Series Forecasting (TsFile format) | |
| Apache TsFile version of [`ChengsenWang/CGTSF`](https://huggingface.co/datasets/ChengsenWang/CGTSF). | |
| Modalities: Time-series. | |
| ## Overview | |
| CGTSF addresses context-guided time-series forecasting by aligning numerical | |
| history and prediction windows with textual background, weather, and date | |
| information. The source provides three logically distinct multimodal domains: | |
| London electricity usage (LEU), Melbourne solar power generation (MSPG), and | |
| Paris traffic flow (PTF). The source card reports the following frequencies and | |
| series counts: | |
| | Config | Rows | Series | Frequency | Array widths (Hist / Pred) | | |
| |---|---:|---:|---|---:| | |
| | LEU | 11,616 | 16 | 30 minutes | 240 / 48 | | |
| | MSPG | 10,557 | 27 | 15 minutes | 480 / 96 | | |
| | PTF | 11,520 | 32 | 1 hour | 120 / 24 | | |
| The three source files remain separate because their serialized array widths | |
| are incompatible. The source card describes PTF as a 2012 traffic dataset, | |
| while the observed source dates at this pinned revision are in 2022; the | |
| conversion follows the source date values without changing them. | |
| ## Schema (TsFile structure) | |
| Each config contains its own table with the same structural roles: | |
| - **Time** (INT64, milliseconds) - the source target-day `Date` at UTC | |
| midnight. | |
| - **idx** (TAG, STRING) - `Idx`, identifying the source series/device. | |
| - **hist_000 ... hist_N** (FIELD, DOUBLE) - the serialized `Hist` numeric | |
| window expanded to scalar fields; `N` depends on the config. | |
| - **pred_000 ... pred_N** (FIELD, DOUBLE) - the serialized `Pred` window | |
| expanded to scalar fields; `N` depends on the config. | |
| - **text** (FIELD, STRING) - the aligned textual context. | |
| `Hist` and `Pred` are parsed with a safe literal/JSON parser and expanded | |
| without changing their numeric values. The source `Date` is represented | |
| losslessly by `Time`; no source row or semantic column is dropped. | |
| ## 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("cgtsf_leu.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()) | |
| ``` | |
| ## Source & license | |
| - **Original dataset:** https://huggingface.co/datasets/ChengsenWang/CGTSF | |
| - **Source revision:** `f85b3fa3a8b3f1a2adbc6edf49f82b7d9d1758c9` | |
| - **Author / publisher:** Chengsen Wang, Qi Qi, Jingyu Wang, Haifeng Sun, Zirui Zhuang, Jinming Wu, Lei Zhang, Jianxin Liao. | |
| - **Paper:** https://arxiv.org/abs/2412.11376 | |
| - **License:** Apache-2.0. | |