Add moving-average smoothing (SMA / WMA / EWMA / rolling)

README.md

@@ -13,6 +13,7 @@
 
 ## Table of Contents
 
+- [What's new (2026-06-22) — Moving-Average Smoothing](#whats-new-2026-06-22--moving-average-smoothing)
 - [What's new (2026-06-22) — Single-Series Anomaly Detection](#whats-new-2026-06-22--single-series-anomaly-detection)
 - [What's new (2026-06-22) — Near-Duplicate Text Detection (SimHash / MinHash)](#whats-new-2026-06-22--near-duplicate-text-detection-simhash--minhash)
 - [What's new (2026-06-22) — String-Distance Similarity Metrics](#whats-new-2026-06-22--string-distance-similarity-metrics)
@@ -154,6 +155,12 @@
 
 ---
 
+## What's new (2026-06-22) — Moving-Average Smoothing
+
+Smooth a noisy value series. Full reference: [`docs/source/Eng/doc/new_features/v102_features_doc.rst`](docs/source/Eng/doc/new_features/v102_features_doc.rst).
+
+- **`sma` / `wma` / `ewma` / `rolling`** (`AC_sma`, `AC_ewma`): `stats.describe` summarizes a whole sample and `timeseries` rolls counters into rates, but nothing smoothed a noisy signal. This adds trailing simple/weighted/exponentially-weighted moving averages and a generic rolling reducer, all returning a same-length list aligned to the input timeline. Pure-stdlib, deterministic.
+
 ## What's new (2026-06-22) — Single-Series Anomaly Detection
 
 Flag the spike in one live metric series. Full reference: [`docs/source/Eng/doc/new_features/v101_features_doc.rst`](docs/source/Eng/doc/new_features/v101_features_doc.rst).

README/README_zh-CN.md

@@ -12,6 +12,7 @@
 
 ## 目录
 
+- [本次更新 (2026-06-22) — 移动平均平滑](#本次更新-2026-06-22--移动平均平滑)
 - [本次更新 (2026-06-22) — 单序列异常检测](#本次更新-2026-06-22--单序列异常检测)
 - [本次更新 (2026-06-22) — 近似重复文本检测(SimHash / MinHash)](#本次更新-2026-06-22--近似重复文本检测simhash--minhash)
 - [本次更新 (2026-06-22) — 字符串距离相似度量](#本次更新-2026-06-22--字符串距离相似度量)
@@ -153,6 +154,12 @@
 
 ---
 
+## 本次更新 (2026-06-22) — 移动平均平滑
+
+平滑噪声值序列。完整参考:[`docs/source/Zh/doc/new_features/v102_features_doc.rst`](../docs/source/Zh/doc/new_features/v102_features_doc.rst)。
+
+- **`sma` / `wma` / `ewma` / `rolling`**(`AC_sma`、`AC_ewma`):`stats.describe` 汇总整个样本,`timeseries` 把计数器滚成速率,但没有东西能平滑噪声信号。本功能加入尾端简单/加权/指数加权移动平均与通用滚动归约器,全部返回与输入时间线对齐的等长 list。纯标准库、确定。
+
 ## 本次更新 (2026-06-22) — 单序列异常检测
 
 标记单一实时度量序列中的尖峰。完整参考:[`docs/source/Zh/doc/new_features/v101_features_doc.rst`](../docs/source/Zh/doc/new_features/v101_features_doc.rst)。

README/README_zh-TW.md

@@ -12,6 +12,7 @@
 
 ## 目錄
 
+- [本次更新 (2026-06-22) — 移動平均平滑](#本次更新-2026-06-22--移動平均平滑)
 - [本次更新 (2026-06-22) — 單序列異常偵測](#本次更新-2026-06-22--單序列異常偵測)
 - [本次更新 (2026-06-22) — 近似重複文字偵測(SimHash / MinHash)](#本次更新-2026-06-22--近似重複文字偵測simhash--minhash)
 - [本次更新 (2026-06-22) — 字串距離相似度量](#本次更新-2026-06-22--字串距離相似度量)
@@ -153,6 +154,12 @@
 
 ---
 
+## 本次更新 (2026-06-22) — 移動平均平滑
+
+平滑雜訊值序列。完整參考:[`docs/source/Zh/doc/new_features/v102_features_doc.rst`](../docs/source/Zh/doc/new_features/v102_features_doc.rst)。
+
+- **`sma` / `wma` / `ewma` / `rolling`**(`AC_sma`、`AC_ewma`):`stats.describe` 彙總整個樣本,`timeseries` 把計數器滾成速率,但沒有東西能平滑雜訊訊號。本功能加入尾端簡單/加權/指數加權移動平均與通用滾動歸約器,全部回傳與輸入時間線對齊的等長 list。純標準函式庫、具決定性。
+
 ## 本次更新 (2026-06-22) — 單序列異常偵測
 
 標記單一即時度量序列中的尖峰。完整參考:[`docs/source/Zh/doc/new_features/v101_features_doc.rst`](../docs/source/Zh/doc/new_features/v101_features_doc.rst)。

docs/source/Eng/doc/new_features/v102_features_doc.rst

@@ -0,0 +1,35 @@
+Moving-Average Smoothing
+========================
+
+``stats.describe`` summarises a whole sample and ``timeseries`` rolls counters
+into rates, but nothing smoothed a noisy signal or weighted recent points. This
+adds trailing simple / weighted / exponentially-weighted moving averages and a
+generic rolling reducer.
+
+Pure standard library; imports no ``PySide6``. Every function is pure (values
+in, list out), so it is fully deterministic in CI.
+
+Headless API
+------------
+
+.. code-block:: python
+
+    from je_auto_control import sma, wma, ewma, rolling
+
+    sma([1, 2, 3, 4], 2)            # [1.0, 1.5, 2.5, 3.5]
+    ewma([1, 2, 3], alpha=0.5)      # [1.0, 1.5, 2.25]
+    wma(values, [1, 2, 3])          # weights align to the latest points
+    rolling(values, 5, max)         # generic trailing-window reduction
+
+``sma`` averages each trailing window of ``window`` points; ``wma`` applies the
+given weights (latest-aligned); ``ewma`` smooths with factor ``alpha`` in
+``(0, 1]``; ``rolling`` applies any reducer over each trailing window. All
+return a same-length list, so the result lines up with the input timeline (a
+``resource_profiler`` FPS/CPU series, a latency stream, etc.).
+
+Executor commands
+-----------------
+
+``AC_sma`` returns ``{series}`` for ``values`` over a ``window``; ``AC_ewma``
+returns ``{series}`` for an ``alpha``. Both are exposed as MCP tools (``ac_sma``
+/ ``ac_ewma``) and as Script Builder commands under **Data**.

docs/source/Eng/eng_index.rst

@@ -124,6 +124,7 @@ Comprehensive guides for all AutoControl features.
    doc/new_features/v99_features_doc
    doc/new_features/v100_features_doc
    doc/new_features/v101_features_doc
+   doc/new_features/v102_features_doc
    doc/ocr_backends/ocr_backends_doc
    doc/observability/observability_doc
    doc/operations_layer/operations_layer_doc

docs/source/Zh/doc/new_features/v102_features_doc.rst

@@ -0,0 +1,29 @@
+移動平均平滑
+==========
+
+``stats.describe`` 彙總整個樣本,``timeseries`` 把計數器滾成速率,但沒有東西能平滑雜訊訊號或加權近期點。
+本功能加入尾端的簡單 / 加權 / 指數加權移動平均,以及一個通用的滾動歸約器。
+
+純標準函式庫;不匯入 ``PySide6``。每個函式皆為純函式(輸入值、輸出 list),因此在 CI 中完全具決定性。
+
+無頭 API
+--------
+
+.. code-block:: python
+
+    from je_auto_control import sma, wma, ewma, rolling
+
+    sma([1, 2, 3, 4], 2)            # [1.0, 1.5, 2.5, 3.5]
+    ewma([1, 2, 3], alpha=0.5)      # [1.0, 1.5, 2.25]
+    wma(values, [1, 2, 3])          # 權重對齊到最新的點
+    rolling(values, 5, max)         # 通用尾端視窗歸約
+
+``sma`` 對每個 ``window`` 點的尾端視窗取平均;``wma`` 套用給定權重(對齊最新);``ewma`` 以 ``(0, 1]`` 的
+``alpha`` 平滑;``rolling`` 對每個尾端視窗套用任意歸約器。全部回傳等長 list,因此結果與輸入時間線對齊
+(``resource_profiler`` FPS/CPU 序列、延遲串流等)。
+
+執行器命令
+----------
+
+``AC_sma`` 對 ``values`` 在 ``window`` 上回傳 ``{series}``;``AC_ewma`` 對 ``alpha`` 回傳 ``{series}``。
+兩者皆以 MCP 工具(``ac_sma`` / ``ac_ewma``)以及 Script Builder 中 **Data** 分類下的命令提供。

docs/source/Zh/zh_index.rst

@@ -124,6 +124,7 @@ AutoControl 所有功能的完整使用指南。
    doc/new_features/v99_features_doc
    doc/new_features/v100_features_doc
    doc/new_features/v101_features_doc
+   doc/new_features/v102_features_doc
    doc/ocr_backends/ocr_backends_doc
    doc/observability/observability_doc
    doc/operations_layer/operations_layer_doc

je_auto_control/__init__.py

@@ -434,6 +434,8 @@
     detect_anomalies, ewma_control, mad_anomalies, mad_scores,
     zscore_anomalies, zscore_scores,
 )
+# Moving-average smoothing (SMA / WMA / EWMA / rolling)
+from je_auto_control.utils.smoothing import ewma, rolling, sma, wma
 # Bulkhead concurrency isolation + rate-limit header parsing
 from je_auto_control.utils.bulkhead import (
     Bulkhead, BulkheadFullError, next_delay, parse_ratelimit, parse_retry_after,
@@ -1005,6 +1007,7 @@ def start_autocontrol_gui(*args, **kwargs):
     "ts_rate", "ts_resample",
     "detect_anomalies", "ewma_control", "mad_anomalies", "mad_scores",
     "zscore_anomalies", "zscore_scores",
+    "ewma", "rolling", "sma", "wma",
     "Bulkhead", "BulkheadFullError", "next_delay", "parse_ratelimit",
     "parse_retry_after",
     "Cassette", "CassetteMissError",

je_auto_control/gui/script_builder/command_schema.py

@@ -1979,6 +1979,22 @@ def _add_resilience_specs(specs: List[CommandSpec]) -> None:
         ),
         description="Flag outliers in a numeric series (MAD / z-score).",
     ))
+    specs.append(CommandSpec(
+        "AC_sma", "Data", "Smoothing: Simple Moving Average",
+        fields=(
+            FieldSpec("values", FieldType.STRING, placeholder="[1, 2, 3, 4, 5]"),
+            FieldSpec("window", FieldType.INT, placeholder="3"),
+        ),
+        description="Trailing simple moving average over a window.",
+    ))
+    specs.append(CommandSpec(
+        "AC_ewma", "Data", "Smoothing: EWMA",
+        fields=(
+            FieldSpec("values", FieldType.STRING, placeholder="[1, 2, 3, 4, 5]"),
+            FieldSpec("alpha", FieldType.FLOAT, optional=True, default=0.3),
+        ),
+        description="Exponentially-weighted moving average of a series.",
+    ))
     specs.append(CommandSpec(
         "AC_diff_rows", "Data", "Dataset Diff: Rows by Key",
         fields=(

je_auto_control/utils/executor/action_executor.py

@@ -3423,6 +3423,24 @@ def _detect_anomalies(values: Any, method: str = "mad",
                                         threshold=threshold)}
 
 
+def _sma(values: Any, window: Any) -> Dict[str, Any]:
+    """Adapter: trailing simple moving average."""
+    import json
+    from je_auto_control.utils.smoothing import sma
+    if isinstance(values, str):
+        values = json.loads(values)
+    return {"series": sma(values, int(window))}
+
+
+def _ewma(values: Any, alpha: Any = 0.3) -> Dict[str, Any]:
+    """Adapter: exponentially-weighted moving average."""
+    import json
+    from je_auto_control.utils.smoothing import ewma
+    if isinstance(values, str):
+        values = json.loads(values)
+    return {"series": ewma(values, alpha=float(alpha))}
+
+
 def _evaluate_slo(records: Any, target: float,
                   window_s: Optional[float] = None) -> Dict[str, Any]:
     """Adapter: SLI + error budget for outcome records (list or JSON string)."""
@@ -4524,6 +4542,8 @@ def __init__(self):
             "AC_ts_rate": _ts_rate,
             "AC_ts_downsample": _ts_downsample,
             "AC_detect_anomalies": _detect_anomalies,
+            "AC_sma": _sma,
+            "AC_ewma": _ewma,
             "AC_detect_drift": _detect_drift,
             "AC_categorical_drift": _categorical_drift,
             "AC_diff_rows": _diff_rows,

je_auto_control/utils/mcp_server/tools/_factories.py

@@ -3548,6 +3548,31 @@ def dataset_diff_tools() -> List[MCPTool]:
     ]
 
 
+def smoothing_tools() -> List[MCPTool]:
+    return [
+        MCPTool(
+            name="ac_sma",
+            description=("Trailing simple moving average of a numeric 'values' "
+                         "series over the last 'window' points. Returns {series}."),
+            input_schema=schema(
+                {"values": {"type": "array"}, "window": {"type": "integer"}},
+                ["values", "window"]),
+            handler=h.sma,
+            annotations=READ_ONLY,
+        ),
+        MCPTool(
+            name="ac_ewma",
+            description=("Exponentially-weighted moving average of 'values' with "
+                         "smoothing factor 'alpha'. Returns {series}."),
+            input_schema=schema(
+                {"values": {"type": "array"}, "alpha": {"type": "number"}},
+                ["values"]),
+            handler=h.ewma,
+            annotations=READ_ONLY,
+        ),
+    ]
+
+
 def anomaly_tools() -> List[MCPTool]:
     return [
         MCPTool(
@@ -5499,7 +5524,7 @@ def media_assert_tools() -> List[MCPTool]:
     secret_ref_tools, config_schema_tools, config_redaction_tools,
     data_profile_tools, http_problem_tools, dotenv_tools,
     sse_client_tools, layered_config_tools, data_drift_tools, schema_compat_tools,
-    timeseries_tools, anomaly_tools,
+    timeseries_tools, anomaly_tools, smoothing_tools,
     dataset_diff_tools, referential_tools, link_header_tools, multipart_tools,
     http_content_tools, cookie_jar_tools, http_conditional_tools,
     saga_tools, decision_table_tools, locator_repair_tools,

je_auto_control/utils/mcp_server/tools/_handlers.py

@@ -1910,6 +1910,16 @@ def detect_anomalies(values, method="mad", threshold=None):
     return _detect_anomalies(values, method, threshold)
 
 
+def sma(values, window):
+    from je_auto_control.utils.executor.action_executor import _sma
+    return _sma(values, window)
+
+
+def ewma(values, alpha=0.3):
+    from je_auto_control.utils.executor.action_executor import _ewma
+    return _ewma(values, alpha)
+
+
 def detect_drift(reference, current, threshold=0.25, bins=10):
     from je_auto_control.utils.executor.action_executor import _detect_drift
     return _detect_drift(reference, current, threshold, bins)

je_auto_control/utils/smoothing/__init__.py

@@ -0,0 +1,4 @@
+"""Moving-average smoothing for AutoControl value series."""
+from je_auto_control.utils.smoothing.smoothing import ewma, rolling, sma, wma
+
+__all__ = ["ewma", "rolling", "sma", "wma"]

je_auto_control/utils/smoothing/smoothing.py

@@ -0,0 +1,57 @@
+"""Moving-average smoothing for noisy value series.
+
+``stats.describe`` summarises a whole sample and ``timeseries`` rolls counters
+into rates, but nothing smooths a noisy signal or weights recent points. This
+adds trailing simple / weighted / exponentially-weighted moving averages and a
+generic rolling reducer, e.g. for a ``resource_profiler`` FPS/CPU timeline.
+
+Pure standard library; imports no ``PySide6``. Every function is pure (values
+in, list out), so it is fully deterministic in CI.
+"""
+from typing import Callable, List, Sequence
+
+
+def sma(values: Sequence[float], window: int) -> List[float]:
+    """Trailing simple moving average over the last ``window`` points."""
+    if window <= 0:
+        raise ValueError("window must be positive")
+    out: List[float] = []
+    for i in range(len(values)):
+        chunk = values[max(0, i - window + 1):i + 1]
+        out.append(sum(chunk) / len(chunk))
+    return out
+
+
+def wma(values: Sequence[float], weights: Sequence[float]) -> List[float]:
+    """Trailing weighted moving average; ``weights`` align to the latest points."""
+    weight_list = list(weights)
+    if not weight_list:
+        raise ValueError("weights must be non-empty")
+    out: List[float] = []
+    for i in range(len(values)):
+        chunk = values[max(0, i - len(weight_list) + 1):i + 1]
+        applied = weight_list[-len(chunk):]
+        out.append(sum(x * w for x, w in zip(chunk, applied)) / sum(applied))
+    return out
+
+
+def ewma(values: Sequence[float], *, alpha: float) -> List[float]:
+    """Exponentially-weighted moving average (smoothing factor ``alpha``)."""
+    if not 0 < alpha <= 1:
+        raise ValueError("alpha must be in (0, 1]")
+    out: List[float] = []
+    previous = None
+    for value in values:
+        previous = value if previous is None else alpha * value + (
+            1 - alpha) * previous
+        out.append(previous)
+    return out
+
+
+def rolling(values: Sequence[float], window: int,
+            func: Callable[[Sequence[float]], float]) -> List[float]:
+    """Apply ``func`` over each trailing window of size ``window``."""
+    if window <= 0:
+        raise ValueError("window must be positive")
+    return [func(values[max(0, i - window + 1):i + 1])
+            for i in range(len(values))]