THULab/ts-arena-weather

Weather (Max Planck Institute, Autoformer) — TsFile format

This repository is a conversion to Apache TsFile format of the Weather time-series dataset, originating from the TS Arena benchmark suite.

• Original dataset (Hugging Face): ts-arena/weather
• Original data source: Autoformer Dataset Collection (Max Planck Institute for Biogeochemistry weather station, 2020)
• License: MIT

Dataset Description

Multivariate weather time series from the Max Planck weather station in 2020, sampled every 10 minutes, with 52,696 time points and 21 weather features — a common benchmark for long-sequence time-series forecasting (LSTF). The dataset is physically split into train / validation / test (70% / 10% / 20%).

File	split	rows
data/train.tsfile	train	36,887
data/val.tsfile	validation	5,269
data/test.tsfile	test	10,540

⚠️ Data is standardized

The feature values in the original dataset are already standardized (zero mean / unit variance, scaler fitted on train only). This repository keeps the standardized values verbatim and does not de-standardize. To recover the original physical quantities, use scaler_params.json at the repo root (containing per-column mean / std / feature_names):

original = standardized * std + mean

Column meanings

Column	Meaning	Type
Time	time index (INT64, from source timestamp_idx, 0/1/2…; the source has no real timestamps)	time column
p_mbar	pressure p (mbar)	FLOAT
T_degC	air temperature T (°C)	FLOAT
Tpot_K	potential temperature Tpot (K)	FLOAT
Tdew_degC	dew point temperature Tdew (°C)	FLOAT
rh_pct	relative humidity rh (%)	FLOAT
VPmax_mbar	saturation vapor pressure VPmax (mbar)	FLOAT
VPact_mbar	actual vapor pressure VPact (mbar)	FLOAT
VPdef_mbar	vapor pressure deficit VPdef (mbar)	FLOAT
sh_g_kg	specific humidity sh (g/kg)	FLOAT
H2OC_mmol_mol	water-vapor concentration H2OC (mmol/mol)	FLOAT
rho_g_m3	air density rho (g/m³)	FLOAT
wv_m_s	wind speed wv (m/s)	FLOAT
max_wv_m_s	max. wind speed wv (m/s)	FLOAT
wd_deg	wind direction wd (deg)	FLOAT
rain_mm	rainfall rain (mm)	FLOAT
raining_s	rainfall duration raining (s)	FLOAT
SWDR_W_m2	shortwave downward radiation SWDR (W/m²)	FLOAT
PAR_umol_m2_s	photosynthetically active radiation PAR (µmol/m²/s)	FLOAT
max_PAR_umol_m2_s	max. PAR (µmol/m²/s)	FLOAT
Tlog_degC	recorded temperature Tlog (°C)	FLOAT
OT	forecast target (Output Target)	FLOAT

Repository structure

ts-arena-weather/
├── README.md           # this file
├── scaler_params.json  # standardization-reversal params (mean / std / feature_names)
└── data/
    ├── train.tsfile
    ├── val.tsfile
    └── test.tsfile

Conversion Notes

• Each split is converted to its own .tsfile, not merged (preserving the original train/val/test split).
• Time column: from the source timestamp_idx (int64, 0/1/2…) as INT64. The source data has no real timestamps.
• Standardized values kept verbatim: no de-standardization; reversal params are in scaler_params.json.
• Field columns: all 21 weather features are stored as single-precision FLOAT, with column names cleaned into TsFile-safe identifiers (units kept in the names). The ²/µ in the original column names were corrupted into U+FFFD and are restored by physical meaning to W_m2 / umol_m2_s.
• No data columns dropped: all 22 columns are kept (21 features + timestamp_idx → Time).
• No TAG columns: each split is an independent time series.

Reading example

from tsfile import TsFileReader

reader = TsFileReader("data/train.tsfile")
schemas = reader.get_all_table_schemas()
tname = next(iter(schemas))
field_cols = [c.get_column_name() for c in schemas[tname].get_columns()]
with reader.query_table(tname, field_cols, batch_size=65536) as rs:
    while (batch := rs.read_arrow_batch()) is not None:
        df = batch.to_pandas()
        print(df.head())
        break

Source and citation

Data from the Max Planck Institute for Biogeochemistry weather station, curated by the Autoformer dataset collection and published (standardized) by TS Arena.

@inproceedings{wu2021autoformer,
  title={Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting},
  author={Wu, Haixu and Xu, Jiehui and Wang, Jianmin and Long, Mingsheng},
  booktitle={Advances in Neural Information Processing Systems},
  year={2021}
}