LSTM神经网络实现对股市收盘价格的预测实战（python实现附源码超详细)

迈不过友情╰ 2023-09-30 16:13 19阅读 0赞

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**由于独特的设计结构 LSTM适合于处理和预测时间序列中间隔和延迟非常长的重要事件。**

**LSTM是一种含有LSTM区块（blocks）或其他的一种类神经网络，文献或其他资料中LSTM区块可能被描述成智能网络单元，因为它可以记忆不定时间长度的数值，区块中有一个gate能够决定input是否重要到能被记住及能不能被输出output**

**LSTM有很多个版本，其中一个重要的版本是GRU（Gated Recurrent Unit），根据谷歌的测试表明，LSTM中最重要的是Forget gate，其次是Input gate，最次是Output gate。**

**介绍完LSTM的基本内容 接下来实战通过LSTM来预测股市收盘价格**

先上结果

#### 1：随着训练次数增加损失函数的图像如下 可以看出基本符合肘部方法 但是局部会产生突变 ####

![89fce4116050451a9c56fa24c2824196.png][]

![af0d9dd2c39a416c8efb2e916fae09d4.png][]

#### 2：预测结果如下 红色的是预测值 蓝色的是真实值 可以看出除了某几个极值点正确率较高 ####

![837c5783005a4c46a17690faf2ecd811.png][]

代码如下

import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    import torch
    import torch.nn as nn
    from torch.utils.data import DataLoader, Dataset
    import os
    os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
    
    
    def read_dataset(dataset_type):
        assert dataset_type == "train" or dataset_type == "test"
        df = pd.read_csv(stock_market_price_{}.csv'.format(dataset_type))  # 读入股票数据
        data = np.array(df['close'])  # 获取收盘价序列
        data = data[::-1]  # 反转，使数据按照日期先后顺序排列
    
        normalize_data = (data - np.mean(data)) / np.std(data)  # 标准化
        normalize_data = normalize_data[:, np.newaxis]  # 增加维度
        X, y = [], []
        for i in range(len(normalize_data) - time_step):
            _x = normalize_data[i:i + time_step]
            _y = normalize_data[i + time_step]
            X.append(_x.tolist())
            y.append(_y.tolist())
        # plt.figure()
        # plt.plot(data)
        # plt.show() # 以折线图展示data
        return X, y
    
    
    # 实验参数设置
    time_step = 7    # 用前七天的数据预测第八天
    hidden_size = 4  # 隐藏层维度
    lstm_layers = 1  # 网络层数
    batch_size = 64  # 每一批次训练多少个样例
    input_size = 1   # 输入层维度
    output_size = 1  # 输出层维度
    lr = 0.05        # 学习率
    
    
    class myDataset(Dataset):
        def __init__(self, x, y):
            self.x = x
            self.y = y
    
        def __getitem__(self, index):
            return torch.Tensor(self.x[index]), torch.Tensor(self.y[index])
    
        def __len__(self):
            return len(self.x)
    
    
    class LSTM(nn.Module):
        def __init__(self, input_size, output_size, hidden_size, device):
            super(LSTM, self).__init__()
            self.input_size=input_size
            self.output_size=output_size
            self.hidden_size=hidden_size
            self.device=device
            def _one(a,b):
                return nn.Parameter(torch.FloatTensor(a,b).to(self.device))
            def _three():
                return(_one(input_size,hidden_size),
                       _one(hidden_size,hidden_size),
                       nn.Parameter(torch.zeros(hidden_size).to(self.device)))
            self.W_xi,self.W_hi,self.b_i=_three()
            self.W_xf, self.W_hf, self.b_f = _three()
            self.W_xo, self.W_ho, self.b_o = _three()
            self.W_xc, self.W_hc, self.b_c = _three()
            self.W_hq=_one(hidden_size,output_size)
            self.b_q=nn.Parameter(torch.zeros(output_size).to(self.device))
            self.params=[self.W_xi,self.W_hi,self.b_i,self.W_xf, self.W_hf, self.b_f, self.W_xo, self.W_ho, self.b_o,self.W_xc, self.W_hc, self.b_c,
                         self.W_hq,self.b_q]
            for param in self.params:
                if param.dim()==2:
                    nn.init.xavier_normal_(param)
    
    
    
    
    
            
    
        def init_lstm_state(self, batch_size):
            return (torch.zeros((batch_size, self.hidden_size), device=self.device),
                    torch.zeros((batch_size, self.hidden_size), device=self.device))
        
    
        def forward(self, seq):
            (H,C)=self.init_lstm_state(seq.shape[0])
            for step in range(seq.shape[1]):
                X=seq[:,step,:]
                I=torch.sigmoid((X@self.W_xi)+(H@self.W_hi)+self.b_i)
                F = torch.sigmoid((X @ self.W_xf) + (H @ self.W_hf) + self.b_f)
                O = torch.sigmoid((X @ self.W_xo) + (H @ self.W_ho) + self.b_o)
                C_tilda=torch.tanh(torch.matmul(X.float(),self.W_xc)+torch.matmul(H.float(),self.W_hc)+self.b_c)
                C=F*C+I*C_tilda
                H=O*torch.tanh(C)
            Y=(H@self.W_hq)+self.b_q
            return Y,(H,C)
    
    
    
    
    X_train, y_train = read_dataset('train')
    X_test, y_test = read_dataset('test')
    train_dataset = myDataset(X_train, y_train)
    test_dataset = myDataset(X_test, y_test)
    train_loader = DataLoader(train_dataset, batch_size, shuffle=True)
    test_loader = DataLoader(test_dataset, 1)
    
    # 设定训练轮数
    num_epochs = 50
    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
    hist = np.zeros(num_epochs)
    model = LSTM(input_size, output_size, hidden_size, device)
    # 定义优化器和损失函数
    optimiser = torch.optim.Adam(model.parameters(), lr=lr)  # 使用Adam优化算法
    loss_func = torch.nn.MSELoss(reduction='mean')  # 使用均方差作为损失函数
    for epoch in range(num_epochs):
        epoch_loss = 0
        for i, data in enumerate(train_loader):
            X, y = data
            pred_y, _ = model(X.to(device))
            loss = loss_func(pred_y, y.to(device))
            optimiser.zero_grad()
            loss.backward()
            optimiser.step()
            epoch_loss += loss.item()
        print("Epoch ", epoch, "MSE: ", epoch_loss)
        hist[epoch] = epoch_loss
    plt.plot(hist)
    plt.show()
    
    # 测试
    model.eval()
    result = []
    for i, data in enumerate(test_loader):
        X, y = data
        pred_y, _ = model(X.to(device))
        result.append(pred_y.item())
    
    plt.plot(range(len(y_test)), y_test, label="true_y", color="blue")
    plt.plot(range(len(result)), result, label="pred_y", color="red")
    plt.legend(loc='best')
    plt.show()

[89fce4116050451a9c56fa24c2824196.png]: https://img-blog.csdnimg.cn/89fce4116050451a9c56fa24c2824196.png
[af0d9dd2c39a416c8efb2e916fae09d4.png]: https://img-blog.csdnimg.cn/af0d9dd2c39a416c8efb2e916fae09d4.png
[837c5783005a4c46a17690faf2ecd811.png]: https://img-blog.csdnimg.cn/837c5783005a4c46a17690faf2ecd811.png