[NOTICKET] fix(trend,forecast): a period with no rows stays empty (P0-8)

- resample().agg("sum") turned a month with no rows into 0: the trend read it as a
collapse and the forecast fitted it as a real value
- New temporal._resample(): sum uses min_count=1, so an empty period is None; count
keeps 0 (zero events is the true count); other aggs were already None
- analyze_forecast uses the same helper, so the empty month is interpolated for the fit
- Example (April empty): trend R^2 0.06 -> 0.90, forecast no longer dips after "April 0"
- Chart already draws None as a gap; the report table shows it as a dash
- Reported by Rifqi (audit P0-8, decision D4)

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

src/tools/analytics/forecast.py

@@ -34,6 +34,7 @@ from src.tools.analytics.temporal import (
     _clean,
     _parse_date_column,
     _period_label,
+    _resample,
 )
 from src.tools.ml.executor import run_fit
 from src.tools.spec_builder import build_description
@@ -221,7 +222,7 @@ async def analyze_forecast(
     dates = pd.Series(_parse_date_column(df, date_column).to_numpy(), name="_date")
     values = pd.Series(df[value_column].to_numpy(), name="_value")
     frame = pd.concat([dates, values], axis=1).dropna(subset=["_date"]).set_index("_date")
-    resampled = frame["_value"].sort_index().resample(FREQ_MAP[freq]).agg(agg).astype(float)
+    resampled = _resample(frame["_value"], freq, agg).astype(float)
 
     observed = resampled.dropna()
     if observed.shape[0] < _MIN_POINTS:

src/tools/analytics/temporal.py

@@ -128,6 +128,19 @@ def _parse_date_column(df: pd.DataFrame, date_column: str) -> pd.Series:
     )
 
 
+def _resample(series: pd.Series, freq: str, agg: str) -> pd.Series:
+    """Aggregate a datetime-indexed series per period; a period with no rows stays empty.
+
+    Plain `resample().agg("sum")` turns a month with no rows into 0, which reads as a
+    collapse in the trend and is fitted as a real value by the forecast. `count` keeps
+    its 0 — zero events in a month is the true count.
+    """
+    resampler = series.sort_index().resample(FREQ_MAP[freq])
+    if agg == "sum":
+        return resampler.sum(min_count=1)
+    return resampler.agg(agg)
+
+
 def _period_label(ts: pd.Timestamp, freq: str) -> str:
     """Human-readable period label keyed off the friendly frequency name."""
     if freq == "month":
@@ -268,7 +281,7 @@ def analyze_trend(
     dates = pd.Series(_parse_date_column(df, date_column).to_numpy(), name="_date")
     values = pd.Series(df[value_column].to_numpy(), name="_value")
     s = pd.concat([dates, values], axis=1).dropna(subset=["_date"]).set_index("_date")
-    resampled = s["_value"].sort_index().resample(FREQ_MAP[freq]).agg(agg)
+    resampled = _resample(s["_value"], freq, agg)
 
     points = [
         {"period": _period_label(ts, freq), "value": _clean(val)}