Datasets:
Tasks:
Time Series Forecasting
Sub-tasks:
univariate-time-series-forecasting
Size:
1K<n<10K
License:
| from typing import Any, Dict, List, Optional | |
| import numpy as np | |
| import pandas as pd | |
| def to_dict( | |
| target_values: np.ndarray, | |
| start: pd.Timestamp, | |
| cat: Optional[List[int]] = None, | |
| item_id: Optional[Any] = None, | |
| real: Optional[np.ndarray] = None, | |
| ) -> Dict: | |
| def serialize(x): | |
| if np.isnan(x): | |
| return "NaN" | |
| else: | |
| # return x | |
| return float("{0:.6f}".format(float(x))) | |
| res = { | |
| "start": start, | |
| "target": [serialize(x) for x in target_values], | |
| } | |
| if cat is not None: | |
| res["feat_static_cat"] = cat | |
| if item_id is not None: | |
| res["item_id"] = item_id | |
| if real is not None: | |
| res["feat_dynamic_real"] = real.astype(np.float32).tolist() | |
| return res | |