卷积神经网络python预测价格

卷积神经网络可以用于股价预测。下面是一个使用卷积神经网络实现股价预测的Python代码示例：

import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense

# 加载并处理数据
data = pd.read_csv('stock_data.csv')
prices = data['price'].values
inputs = []
outputs = []
window_size = 10

for i in range(len(prices) - window_size):
    inputs.append(prices[i:i+window_size])
    outputs.append(prices[i+window_size])

inputs = np.array(inputs).reshape(-1, window_size, 1)
outputs = np.array(outputs)

# 构建卷积神经网络模型
model = Sequential()
model.add(Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(window_size, 1)))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(1))

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

# 训练模型
model.fit(inputs, outputs, epochs=10, batch_size=32)

# 使用模型进行预测
prediction = model.predict(inputs[-1].reshape(1, window_size, 1))

# 打印预测结果
print("预测价格为:", prediction[0][0])

请注意，上述代码示例仅为演示目的。在实际应用中，你需要根据实际情况对代码进行适当的修改和调整。