feat: new file

backend/app/services/timeseries.py

@@ -0,0 +1,21 @@
+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
+