[NOTICKET] feat(trend,forecast): row count per period and stub flag (P0-8)

- Each trend point and forecast history point now carries `n` (rows) and `stub`
- A stub has fewer rows than half the median non-empty period (needs >= 3 periods);
empty periods are not stubs
- analyze_trend leaves stubs out of first/last, direction, slope and R^2, with a caveat
naming them; it falls back to all periods if every one is a stub
- analyze_forecast still fits every period but caveats a thin last actual period
- IPR OB LOADER (Sep 9 rows vs 63-66): sum(mtbs) "down" -> "up" (R^2 0.01 -> 0.91);
mean(pa) last 99.95 (Sep) -> 92.45 (Jul)
- Reported by Rifqi (audit P0-8, decision D4)

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

src/tools/analytics/forecast.py

@@ -35,6 +35,7 @@ from src.tools.analytics.temporal import (
     _parse_date_column,
     _period_label,
     _resample,
+    _stub_periods,
 )
 from src.tools.ml.executor import run_fit
 from src.tools.spec_builder import build_description
@@ -193,9 +194,9 @@ async def analyze_forecast(
     ordinary synchronous compute. `data` is materialised upstream (Pattern A) and
     arrives as `df`.
 
-    Returns a dict with: freq, agg, horizon, model, history + forecast point lists,
-    last_actual / forecast_end / change_abs / change_pct / direction, and `charts`
-    (the reserved auto-chart key).
+    Returns a dict with: freq, agg, horizon, model, history (with per-period `n` and
+    `stub`) + forecast point lists, last_actual / forecast_end / change_abs /
+    change_pct / direction, caveats, and `charts` (the reserved auto-chart key).
 
     Raises (all wrapped by the invoker's never-throw seam):
         ColumnNotFoundError, InvalidFrequencyError, UnsupportedAggregationError,
@@ -241,9 +242,26 @@ async def analyze_forecast(
     future_index = pd.date_range(
         start=filled.index[-1], periods=horizon + 1, freq=FREQ_MAP[freq]
     )[1:]
+    counts = _resample(frame["_value"], freq, "count")
+    stubs, median_n = _stub_periods(counts)
     history = [
-        {"period": _period_label(ts, freq), "value": _clean(val)} for ts, val in resampled.items()
+        {
+            "period": _period_label(ts, freq),
+            "value": _clean(val),
+            "n": int(counts[ts]),
+            "stub": ts in stubs,
+        }
+        for ts, val in resampled.items()
     ]
+    # The fit still uses every period; a thin last period is the base the projection
+    # starts from, so say so rather than drop it (audit P0-8).
+    caveats: list[str] = []
+    if observed.index[-1] in stubs and median_n is not None:
+        caveats.append(
+            f"The last actual period {_period_label(observed.index[-1], freq)} rests on "
+            f"{int(counts[observed.index[-1]])} rows against a median of {median_n:g} — "
+            "the forecast starts from a partly covered period."
+        )
     forecast_pts = [
         {"period": _period_label(ts, freq), "value": val}
         for ts, val in zip(future_index, fc_values, strict=True)
@@ -272,6 +290,7 @@ async def analyze_forecast(
         "change_abs": change_abs,
         "change_pct": change_pct,
         "direction": direction,
+        "caveats": caveats,
         # Reserved `quality` block (conventions §3): the tool reports its own score and
         # its own verdict; CK7 surfaces `weak`. An unvalidated forecast (series too
         # short to hold anything out) counts as weak — shipping a projection nobody

src/tools/analytics/temporal.py

@@ -45,6 +45,13 @@ SUPPORTED_AGGS = ("sum", "mean", "count", "min", "max", "median")
 # reported direction is then flagged as weakly supported (D-7).
 _WEAK_TREND_R2 = 0.3
 
+# A period with fewer rows than this share of the median period is a stub: a month
+# the data only partly covers. Audit P0-8: a September with 9 rows (other months
+# 63-66) set the first/last comparison and the answer said "turun".
+_STUB_SHARE = 0.5
+# Below this many non-empty periods a median says too little to call one short.
+_STUB_MIN_PERIODS = 3
+
 
 class InvalidFrequencyError(ValueError):
     """The requested period is not in FREQ_MAP (maps to error_code INVALID_FREQUENCY)."""
@@ -141,6 +148,19 @@ def _resample(series: pd.Series, freq: str, agg: str) -> pd.Series:
     return resampler.agg(agg)
 
 
+def _stub_periods(counts: pd.Series) -> tuple[set[pd.Timestamp], float | None]:
+    """Periods whose row count is below `_STUB_SHARE` of the median non-empty period.
+
+    Returns (stub period timestamps, that median). Empty periods are not stubs — they
+    have no value to mislead with.
+    """
+    filled = counts[counts > 0]
+    if filled.shape[0] < _STUB_MIN_PERIODS:
+        return set(), None
+    median = float(filled.median())
+    return set(filled[filled < median * _STUB_SHARE].index), median
+
+
 def _period_label(ts: pd.Timestamp, freq: str) -> str:
     """Human-readable period label keyed off the friendly frequency name."""
     if freq == "month":
@@ -238,8 +258,10 @@ def analyze_trend(
     Returns:
         dict with:
             freq, agg, value_column — echo of the chosen settings
-            points             — [{"period": str, "value": number|None}, ...]
-            first, last        — value of the first/last non-empty period
+            points             — [{"period": str, "value": number|None, "n": rows,
+                                   "stub": bool}, ...]; a stub has fewer than half
+                                   the median period's rows
+            first, last        — value of the first/last non-empty, non-stub period
             change_abs         — last - first
             change_pct         — (last - first) / first, or None if first == 0
             direction          — "up" | "down" | "flat" (endpoints: last vs first)
@@ -282,14 +304,25 @@ def analyze_trend(
     values = pd.Series(df[value_column].to_numpy(), name="_value")
     s = pd.concat([dates, values], axis=1).dropna(subset=["_date"]).set_index("_date")
     resampled = _resample(s["_value"], freq, agg)
+    counts = _resample(s["_value"], freq, "count")
+    stubs, median_n = _stub_periods(counts)
 
     points = [
-        {"period": _period_label(ts, freq), "value": _clean(val)}
+        {
+            "period": _period_label(ts, freq),
+            "value": _clean(val),
+            "n": int(counts[ts]),
+            "stub": ts in stubs,
+        }
         for ts, val in resampled.items()
     ]
 
-    # Summary stats are computed over non-empty periods only.
+    # Summary stats are computed over non-empty periods only, and leave stubs out so a
+    # thinly covered month cannot set the direction (unless nothing else is left).
     non_null = resampled.dropna()
+    full = non_null[~non_null.index.isin(list(stubs))]
+    if not full.empty:
+        non_null = full
     first: float | None
     last: float | None
     change_abs: float | None
@@ -329,6 +362,14 @@ def analyze_trend(
     # On a noisy series they can disagree, and neither is wrong — so say so rather than
     # letting the assembler assert one. R^2 also lets it qualify a weak trend.
     caveats: list[str] = []
+    if stubs and median_n is not None:
+        thin = ", ".join(
+            f"{_period_label(ts, freq)} (n={int(counts[ts])})" for ts in sorted(stubs)
+        )
+        caveats.append(
+            f"Partly covered period(s): {thin}, against a median of {median_n:g} rows per "
+            "period. Shown in the points, but left out of first/last, direction and slope."
+        )
     if slope is not None and change_abs is not None and change_abs != 0 and slope != 0:
         if (change_abs > 0) != (slope > 0):
             caveats.append(