transformer模型时间序列预测代码

### 使用Transformer模型进行时间序列预测的Python实现

对于时间序列预测，特别是针对流感病例数目的预测，可以采用基于Transformer架构的模型来捕捉长时间依赖关系并提高预测准确性[^1]。下面提供了一个简化版的时间序列预测代码示例，该例子展示了如何构建一个基本的Transformer模型用于此类任务。

import numpy as np
import pandas as pd
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Dropout, LayerNormalization
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt


class TransformerBlock(tf.keras.layers.Layer):
    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):
        super(TransformerBlock, self).__init__()
        from tensorflow.keras.layers.experimental.preprocessing import MultiHeadAttention
        
        self.att = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
        self.ffn = tf.keras.Sequential(
            [Dense(ff_dim, activation="relu"), Dense(embed_dim),]
        )
        self.layernorm1 = LayerNormalization(epsilon=1e-6)
        self.layernorm2 = LayerNormalization(epsilon=1e-6)
        self.dropout1 = Dropout(rate)
        self.dropout2 = Dropout(rate)

    def call(self, inputs, training):
        attn_output = self.att(inputs, inputs)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layernorm1(inputs + attn_output)
        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        return self.layernorm2(out1 + ffn_output)


def build_transformer_model(input_shape=(None, 1)):
    input_layer = Input(shape=input_shape)
    
    transformer_block_1 = TransformerBlock(embed_dim=32, num_heads=8, ff_dim=32)(input_layer)
    dense_out = Dense(1)(transformer_block_1)
    
    model = Model(inputs=[input_layer], outputs=[dense_out])
    opt = Adam()
    model.compile(optimizer=opt, loss='mse')
    
    return model


# 假设我们有一个训练集 `X_train` 和标签 `y_train`
model = build_transformer_model()

history = model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2)

plt.plot(history.history['loss'], label='train_loss')
plt.plot(history.history['val_loss'], label='validation_loss')
plt.title('Model Loss Over Epochs')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(loc='upper right')
plt.show()

此段代码定义了一个简单的变压器层，并将其应用于一维输入（即单变量时间序列）。为了适应具体应用场景下的多变量时间序列或其他复杂情况，可能还需要调整网络结构、优化参数设置等细节部分。