import numpy as np

def track_anomalies(anomaly_counts):
    """Track anomaly counts and return statistics."""
    if not anomaly_counts:
        return {"mean": 0, "std": 0}
    mean = np.mean(anomaly_counts)
    std = np.std(anomaly_counts)
    return {"mean": mean, "std": std}

def predict_anomaly(anomaly_counts, threshold=2.5):
    """Predict potential issues based on anomaly spikes."""
    if len(anomaly_counts) < 5:  # Need enough data for prediction
        return False
    stats = track_anomalies(anomaly_counts)
    recent_counts = anomaly_counts[-5:]  # Last 5 frames
    # Flag a potential issue if the recent anomaly count is significantly higher than the mean
    for count in recent_counts:
        if stats["std"] > 0 and (count - stats["mean"]) / stats["std"] > threshold:
            return True
    return False