from __future__ import annotations

import pandas as pd


def normalize_timeseries(df: pd.DataFrame, ts_col: str, value_cols: list[str]) -> list[dict]:
    df = df.copy()
    df[ts_col] = pd.to_datetime(df[ts_col], errors="coerce")
    df = df.dropna(subset=[ts_col]).sort_values(ts_col)
    out = []
    for _, r in df.iterrows():
        p = {"ds": r[ts_col].to_pydatetime().isoformat()}
        for c in value_cols:
            v = r.get(c)
            try:
                p[c] = float(v) if v is not None else 0.0
            except Exception:
                p[c] = 0.0
        out.append(p)
    return out