Datasets:
Formats:
json
Languages:
English
Size:
< 1K
Tags:
time-series
time-series-decomposition
benchmark
component-recovery
symbolic-regression
icml-2026
License:
File size: 1,922 Bytes
17b7ba4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | import pandas as pd
import numpy as np
from typing import Union, Dict, Any
from pathlib import Path
from .core import DecompResult
def read_series(path: Union[str, Path], col: str = None) -> np.ndarray:
"""
Read time series from CSV or Parquet.
"""
path = Path(path)
if path.suffix == ".csv":
df = pd.read_csv(path)
elif path.suffix == ".parquet":
df = pd.read_parquet(path)
else:
raise ValueError(f"Unsupported file format: {path.suffix}")
if col:
return df[col].values
else:
# Try to find a numeric column or use the first one
numeric_cols = df.select_dtypes(include=[np.number]).columns
if len(numeric_cols) > 0:
return df[numeric_cols[0]].values
else:
return df.iloc[:, 0].values
def save_result(result: DecompResult, out_dir: Union[str, Path], name: str):
"""
Save decomposition result to CSV/Parquet and metadata to JSON.
"""
out_dir = Path(out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
# Save components
data = {
"trend": result.trend,
"season": result.season,
"residual": result.residual
}
for k, v in result.components.items():
data[k] = v
df = pd.DataFrame(data)
df.to_csv(out_dir / f"{name}_components.csv", index=False)
# Save meta
import json
class NumpyEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
if isinstance(obj, np.floating):
return float(obj)
if isinstance(obj, np.ndarray):
return obj.tolist()
return super(NumpyEncoder, self).default(obj)
with open(out_dir / f"{name}_meta.json", "w") as f:
json.dump(result.meta, f, indent=2, cls=NumpyEncoder)
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