有一组时序数据，是企业设备的振动能量大小，一般都稳定在一个范围内，如果能量值出现异常趋势，希望能识别出来，请用Python写算法实现，给出至少五个实现的代码

1. 使用统计方法：计算均值和标准差，判断是否超过一定的阈值

import numpy as np

def detect_anomaly(data, threshold=3):
    mean = np.mean(data)
    std = np.std(data)
    if np.abs(data[-1] - mean) > threshold * std:
        return True
    else:
        return False

2. 使用滑动窗口：对一段时间内的数据进行平均，判断当前数据是否与平均值相差较大

def detect_anomaly(data, window_size=5, threshold=3):
    if len(data) < window_size:
        return False
    else:
        window = data[-window_size:]
        mean = np.mean(window)
        std = np.std(window)
        if np.abs(data[-1] - mean) > threshold * std:
            return True
        else:
            return False

3. 使用聚类方法：将数据聚为正常和异常两类，使用新数据判断所属类别

from sklearn.cluster import KMeans

def detect_anomaly(data, n_clusters=2):
    kmeans = KMeans(n_clusters=n_clusters)
    kmeans.fit(data.reshape(-1, 1))
    label = kmeans.predict(data[-1].reshape(-1, 1))
    if label == 1:
        return True
    else:
        return False

4. 使用支持向量机(SVM)方法：将数据分为正常和异常两类，使用SVM分类器判断新数据所属类别

from sklearn.svm import SVC

def detect_anomaly(data, C=1.0, kernel='rbf'):
    X = np.arange(len(data)).reshape(-1, 1)
    y = np.zeros(len(data))
    for i in range(len(data)):
        if data[i] > np.percentile(data, 95):
            y[i] = 1
    clf = SVC(C=C, kernel=kernel)
    clf.fit(X, y)
    label = clf.predict(len(data).reshape(-1, 1))
    if label == 1:
        return True
    else:
        return False

5. 使用深度学习方法：使用LSTM神经网络进行时序数据预测，判断当前数据是否与预测值相差较大

from keras.models import Sequential
from keras.layers import LSTM, Dense

def detect_anomaly(data, look_back=1, threshold=3):
    model = Sequential()
    model.add(LSTM(4, input_shape=(1, look_back)))
    model.add(Dense(1))
    model.compile(loss='mean_squared_error', optimizer='adam')
    X, y = [], []
    for i in range(len(data)-look_back):
        X.append(data[i:i+look_back])
        y.append(data[i+look_back])
    X = np.array(X)
    y = np.array(y)
    model.fit(X, y, epochs=100, batch_size=1, verbose=0)
    predict = model.predict(np.array(data[-look_back:]).reshape(1, 1, look_back))[0][0]
    if np.abs(data[-1] - predict) > threshold:
        return True
    else:
        return False