机器学习在网络流量预测中的应用：运维人员的智慧水晶球？

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler

# 读取数据
data = pd.read_csv("traffic_data.csv", parse_dates=['timestamp'], index_col='timestamp')

# 归一化处理
scaler = MinMaxScaler()
data["traffic"] = scaler.fit_transform(data["traffic"].values.reshape(-1, 1))

# 可视化流量趋势
data.plot(figsize=(10, 5))
plt.title("Network Traffic Over Time")
plt.show()

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

def create_sequences(data, seq_length=10):
    sequences, labels = [], []
    for i in range(len(data) - seq_length):
        sequences.append(data[i:i + seq_length])
        labels.append(data[i + seq_length])
    return np.array(sequences), np.array(labels)

# 创建训练数据
seq_length = 10
data_values = data["traffic"].values
X, y = create_sequences(data_values, seq_length)

# 重新塑形，符合 LSTM 输入格式
X = X.reshape((X.shape[0], X.shape[1], 1))

# 构建 LSTM 模型
model = Sequential([
    LSTM(50, return_sequences=True, input_shape=(seq_length, 1)),
    LSTM(50),
    Dense(1)
])

model.compile(optimizer='adam', loss='mse')

# 训练模型
model.fit(X, y, epochs=20, batch_size=16, verbose=1)

# 预测未来流量
future_steps = 30
def predict_future(model, data, future_steps):
    predictions = []
    current_seq = data[-seq_length:].tolist()
    for _ in range(future_steps):
        pred = model.predict(np.array(current_seq).reshape(1, seq_length, 1))
        predictions.append(pred[0, 0])
        current_seq.append(pred[0, 0])
        current_seq.pop(0)
    return predictions

future_traffic = predict_future(model, data_values, future_steps)

# 反归一化
data_predicted = scaler.inverse_transform(np.array(future_traffic).reshape(-1, 1))

# 画出预测结果
plt.plot(data.index[-100:], scaler.inverse_transform(data_values[-100:].reshape(-1, 1)), label="Actual Traffic")
plt.plot(pd.date_range(data.index[-1], periods=future_steps, freq="H"), data_predicted, label="Predicted Traffic", linestyle="dashed")
plt.legend()
plt.title("Network Traffic Prediction")
plt.show()