"""趋势预测 + 异常检测。"""

import statistics
from datetime import datetime, timezone


def predict_trend(historical_scores: list[float], months_ahead: int = 3) -> dict:
    """基于历史评分预测未来健康度。"""
    if len(historical_scores) < 2:
        return {"predicted": [], "confidence": 0.0}

    # 简单线性回归
    n = len(historical_scores)
    x_mean = sum(range(n)) / n
    y_mean = sum(historical_scores) / n

    numerator = sum((i - x_mean) * (y - y_mean) for i, y in enumerate(historical_scores))
    denominator = sum((i - x_mean) ** 2 for i in range(n))

    if denominator == 0:
        slope = 0
    else:
        slope = numerator / denominator

    intercept = y_mean - slope * x_mean

    predicted = [slope * (n + i) + intercept for i in range(months_ahead)]
    predicted = [max(0, min(100, p)) for p in predicted]

    # 置信度基于历史数据量
    confidence = min(0.9, n / 12)

    return {
        "predicted": [round(p, 1) for p in predicted],
        "slope": round(slope, 2),
        "confidence": round(confidence, 2),
    }


def detect_anomalies(values: list[float], threshold: float = 2.0) -> list[int]:
    """统计方法识别指标突变。"""
    if len(values) < 3:
        return []

    mean = statistics.mean(values)
    stdev = statistics.stdev(values)

    if stdev == 0:
        return []

    anomalies: list[int] = []
    for i, v in enumerate(values):
        z_score = abs(v - mean) / stdev
        if z_score > threshold:
            anomalies.append(i)

    return anomalies