使用python制作一个微博热度预测实验完整代码

在Python中，制作微博热度预测实验通常涉及时间序列分析，可以使用ARIMA模型或更现代的机器学习方法如LSTM（长短期记忆网络）。这里提供一个简单的时间序列预测框架，假设使用的是pandas和sklearn库：

import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from statsmodels.tsa.arima.model import ARIMA
from keras.models import Sequential
from keras.layers import LSTM, Dense
from sklearn.metrics import mean_squared_error

# 读取微博数据（假定数据存储在一个CSV文件中）
df = pd.read_csv('weibo_data.csv', parse_dates=['date'], index_col='date')

# 准备数据（这里假设最后一列是微博热度）
target_column = df.columns[-1]
data = df[target_column].values.reshape(-1, 1)

# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data)

# 划分训练集和测试集
train_size = int(len(scaled_data) * 0.7)
test_size = len(scaled_data) - train_size
train, test = scaled_data[0:train_size], scaled_data[train_size:]

# 对数据进行滑动窗口划分
def windowed_dataset(series, window_size=1):
    X, y = [], []
    for i in range(len(series) - window_size):
        X.append(series[i:(i + window_size)])
        y.append(series[(i + window_size):])
    return np.array(X), np.array(y)

window_size = 10  # 可能需要调整
X_train, y_train = windowed_dataset(train, window_size)
X_test, y_test = windowed_dataset(test, window_size)

# ARIMA模型预测
arima_model = ARIMA(order=(5,1,0))  # 需要尝试不同的参数组合
arima_model_fit = arima_model.fit(X_train)
arima_pred = arima_model_fit.forecast(steps=len(y_test))[0]

# LSTM模型预测
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(window_size, 1)))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X_train, y_train, epochs=100, verbose=0)
lstm_pred = model.predict(X_test)

# 结果评估
arima_mse = mean_squared_error(y_test, arima_pred)
lstm_mse = mean_squared_error(y_test, lstm_pred)

print(f"ARIMA MSE: {arima_mse}, LSTM MSE: {lstm_mse}")