sklearn中的transformer时间序列预测模型

很抱歉，根据提供的引用内容，没有提到sklearn中的transformer时间序列预测模型。但是，可以介绍一下sklearn中的时间序列预测模型。

sklearn中的时间序列预测模型主要包括ARIMA模型和SARIMA模型。ARIMA模型是一种基于时间序列自回归和移动平均的模型，可以用于预测未来的趋势。SARIMA模型是ARIMA模型的扩展，可以处理季节性时间序列数据。

下面是一个使用ARIMA模型进行时间序列预测的例子：

from statsmodels.tsa.arima_model import ARIMA
import pandas as pd

# 读取数据
data = pd.read_csv('data.csv', header=None, names=['value'])

# 拟合ARIMA模型
model = ARIMA(data, order=(1, 1, 1))
model_fit = model.fit(disp=0)

# 预测未来10个时间点的值
forecast = model_fit.forecast(steps=10)[0]

# 输出预测结果
print(forecast)

相关问题

transformer时间序列预测-多变量多步长时间序列预测代码

以下是一个使用 Transformer 模型进行多变量多步长时间序列预测的代码示例：

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping
from sklearn.preprocessing import StandardScaler
import numpy as np

# 定义 Transformer 模型
def transformer_model(input_shape, output_shape, num_heads, dff, max_seq_len):
    inputs = Input(shape=input_shape)
    x = inputs
    x = Dense(64, activation='relu')(x)
    x = Dropout(0.2)(x)

    # 构建多头注意力机制
    query = Dense(dff)(x)
    key = Dense(dff)(x)
    value = Dense(dff)(x)
    attention = tf.keras.layers.Attention()([query, key, value])
    attention = Dropout(0.2)(attention)
    attention = Dense(64, activation='relu')(attention)

    # 堆叠多个 Transformer 块
    for _ in range(num_heads):
        transformer_block = tf.keras.layers.Transformer(
            num_layers=2, d_model=dff, num_heads=8, 
            activation='relu', dropout=0.2
        )
        x = transformer_block(attention)

    # 输出层
    outputs = Dense(output_shape)(x)
    model = Model(inputs=inputs, outputs=outputs)
    return model

# 定义获取数据的函数
def get_data():
    # 生成随机时间序列数据
    data = np.random.rand(100, 4, 10)
    train_data = data[:80]
    val_data = data[80:]
    return train_data, val_data

# 定义训练函数
def train_model(model, train_data, val_data, epochs):
    # 数据标准化
    scaler = StandardScaler()
    train_data = scaler.fit_transform(train_data.reshape(-1, train_data.shape[-1])).reshape(train_data.shape)
    val_data = scaler.transform(val_data.reshape(-1, val_data.shape[-1])).reshape(val_data.shape)

    # 定义优化器和损失函数
    optimizer = Adam(lr=1e-4)
    loss = 'mean_squared_error'

    # 编译模型
    model.compile(optimizer=optimizer, loss=loss)

    # 定义早停回调函数
    early_stopping = EarlyStopping(monitor='val_loss', patience=5)

    # 训练模型
    model.fit(train_data, train_data, 
        validation_data=(val_data, val_data), 
        epochs=epochs, 
        callbacks=[early_stopping])

# 获取数据
train_data, val_data = get_data()

# 定义模型参数
input_shape = (train_data.shape[1], train_data.shape[2])
output_shape = train_data.shape[2]
num_heads = 4
dff = 64
max_seq_len = train_data.shape[1]

# 构建 Transformer 模型
model = transformer_model(input_shape, output_shape, num_heads, dff, max_seq_len)

# 训练模型
train_model(model, train_data, val_data, epochs=100)

在这个示例中，我们定义了一个 `transformer_model()` 函数来构建 Transformer 模型。该函数接受输入形状、输出形状、头数、dff、最大序列长度等参数，并返回一个 Keras 模型。在我们的示例中，我们使用该模型进行多变量多步长时间序列预测。

我们还定义了一个 `get_data()` 函数来获取数据。在这个示例中，我们生成了一些随机的时间序列数据，并将其拆分为训练数据和验证数据。

最后，我们定义了一个 `train_model()` 函数来训练模型。在这个函数中，我们对数据进行了标准化，并使用 Adam 优化器和均方误差损失函数编译模型。我们还使用早停回调函数来防止模型过拟合。最终，我们调用 `fit()` 方法来训练模型。

Tansformer时间序列预测模型代码

以下是使用 Transformer 模型进行时间序列预测的 Python 代码示例：

import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.preprocessing import MinMaxScaler

# 加载数据
data = pd.read_csv('data.csv', header=None)
dataset = data.values.astype('float32')

# 数据归一化
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

# 定义函数获取输入输出数据
def create_dataset(dataset, look_back=1):
    X, Y = [], []
    for i in range(len(dataset) - look_back - 1):
        a = dataset[i:(i + look_back), 0]
        X.append(a)
        Y.append(dataset[i + look_back, 0])
    return np.array(X), np.array(Y)

# 创建训练和测试数据集
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size, :], dataset[train_size:len(dataset), :]

look_back = 3
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

# 转换数据为张量
trainX = tf.convert_to_tensor(trainX)
trainY = tf.convert_to_tensor(trainY)
testX = tf.convert_to_tensor(testX)
testY = tf.convert_to_tensor(testY)

# 定义 Transformer 模型
class Transformer(tf.keras.Model):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, maximum_position_encoding, rate=0.1):
        super(Transformer, self).__init__()

        self.d_model = d_model
        self.embedding = tf.keras.layers.Dense(d_model, activation='relu')
        self.pos_encoding = positional_encoding(maximum_position_encoding, d_model)

        self.encoder = Encoder(num_layers, d_model, num_heads, dff, rate)

        self.final_layer = tf.keras.layers.Dense(1)

    def call(self, x, training):
        seq_len = tf.shape(x)[1]
        x = self.embedding(x)
        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x += self.pos_encoding[:, :seq_len, :]
        x = self.encoder(x, training)
        x = self.final_layer(x)
        return x

def positional_encoding(position, d_model):
    angle_rads = get_angles(np.arange(position)[:, np.newaxis], np.arange(d_model)[np.newaxis, :], d_model)
    # 对数组中的偶数元素使用 sin 函数
    sines = np.sin(angle_rads[:, 0::2])
    # 对数组中的奇数元素使用 cos 函数
    cosines = np.cos(angle_rads[:, 1::2])
    pos_encoding = np.concatenate([sines, cosines], axis=-1)
    pos_encoding = pos_encoding[np.newaxis, ...]
    return tf.cast(pos_encoding, dtype=tf.float32)

def get_angles(pos, i, d_model):
    angle_rates = 1 / np.power(10000, (2 * (i // 2)) / np.float32(d_model))
    return pos * angle_rates

class MultiHeadAttention(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads):
        super(MultiHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.d_model = d_model

        assert d_model % self.num_heads == 0

        self.depth = d_model // self.num_heads

        self.wq = tf.keras.layers.Dense(d_model)
        self.wk = tf.keras.layers.Dense(d_model)
        self.wv = tf.keras.layers.Dense(d_model)

        self.dense = tf.keras.layers.Dense(d_model)

    def split_heads(self, x, batch_size):
        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
        return tf.transpose(x, perm=[0, 2, 1, 3])

    def call(self, v, k, q, mask):
        batch_size = tf.shape(q)[0]

        q = self.wq(q)
        k = self.wk(k)
        v = self.wv(v)

        q = self.split_heads(q, batch_size)
        k = self.split_heads(k, batch_size)
        v = self.split_heads(v, batch_size)

        scaled_attention, attention_weights = scaled_dot_product_attention(q, k, v, mask)

        scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])
        concat_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model))

        output = self.dense(concat_attention)

        return output, attention_weights

def scaled_dot_product_attention(q, k, v, mask):
    matmul_qk = tf.matmul(q, k, transpose_b=True)

    dk = tf.cast(tf.shape(k)[-1], tf.float32)
    scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)

    if mask is not None:
        scaled_attention_logits += (mask * -1e9)

    attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)

    output = tf.matmul(attention_weights, v)

    return output, attention_weights

class PointWiseFeedForwardNetwork(tf.keras.layers.Layer):
    def __init__(self, d_model, dff):
        super(PointWiseFeedForwardNetwork, self).__init__()
        self.dense1 = tf.keras.layers.Dense(dff, activation='relu')
        self.dense2 = tf.keras.layers.Dense(d_model)

    def call(self, x):
        x = self.dense1(x)
        x = self.dense2(x)
        return x

class EncoderLayer(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads, dff, rate=0.1):
        super(EncoderLayer, self).__init__()

        self.mha = MultiHeadAttention(d_model, num_heads)
        self.ffn = PointWiseFeedForwardNetwork(d_model, dff)

        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)

        self.dropout1 = tf.keras.layers.Dropout(rate)
        self.dropout2 = tf.keras.layers.Dropout(rate)

    def call(self, x, training, mask=None):
        attn_output, _ = self.mha(x, x, x, mask)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layernorm1(x + attn_output)

        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        out2 = self.layernorm2(out1 + ffn_output)

        return out2

class Encoder(tf.keras.layers.Layer):
    def __init__(self, num_layers, d_model, num_heads, dff, maximum_position_encoding, rate=0.1):
        super(Encoder, self).__init__()

        self.d_model = d_model
        self.num_layers = num_layers

        self.embedding = tf.keras.layers.Dense(d_model, activation='relu')
        self.pos_encoding = positional_encoding(maximum_position_encoding, d_model)

        self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate) for _ in range(num_layers)]

        self.dropout = tf.keras.layers.Dropout(rate)

    def call(self, x, training, mask=None):
        seq_len = tf.shape(x)[1]

        x = self.embedding(x)
        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x += self.pos_encoding[:, :seq_len, :]

        x = self.dropout(x, training=training)

        for i in range(self.num_layers):
            x = self.enc_layers[i](x, training, mask)

        return x

# 定义模型参数
num_layers = 4
d_model = 128
dff = 512
num_heads = 8
input_vocab_size = len(trainX)
dropout_rate = 0.1
maximum_position_encoding = len(trainX)

# 创建 Transformer 模型
model = Transformer(num_layers, d_model, num_heads, dff, input_vocab_size, maximum_position_encoding, dropout_rate)

# 定义优化器和损失函数
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
loss_object = tf.keras.losses.MeanSquaredError()

# 定义评估指标
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.MeanAbsoluteError(name='train_mae')
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.MeanAbsoluteError(name='test_mae')

# 定义训练函数
@tf.function
def train_step(inputs, targets):
    with tf.GradientTape() as tape:
        predictions = model(inputs, True)
        loss = loss_object(targets, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

    train_loss(loss)
    train_accuracy(targets, predictions)

# 定义测试函数
@tf.function
def test_step(inputs, targets):
    predictions = model(inputs, False)
    t_loss = loss_object(targets, predictions)

    test_loss(t_loss)
    test_accuracy(targets, predictions)

# 训练模型
EPOCHS = 100
for epoch in range(EPOCHS):
    for i in range(len(trainX)):
        train_step(trainX[i], trainY[i])

    for i in range(len(testX)):
        test_step(testX[i], testY[i])

    template = 'Epoch {}, Loss: {}, MAE: {}, Test Loss: {}, Test MAE: {}'
    print(template.format(epoch + 1, train_loss.result(), train_accuracy.result(), test_loss.result(), test_accuracy.result()))

# 使用模型进行预测
predictions = []
for i in range(len(testX)):
    prediction = model(testX[i], False)
    predictions.append(prediction.numpy()[0][0])

# 反归一化预测结果
predictions = scaler.inverse_transform(np.array(predictions).reshape(-1, 1))

# 反归一化真实结果
testY = scaler.inverse_transform(testY.reshape(-1, 1))

# 输出预测结果和真实结果
for i in range(len(predictions)):
    print('Predicted:', predictions[i][0], 'Actual:', testY[i][0])

在上述代码中，我们首先加载了时间序列数据，然后对数据进行归一化处理。接着，我们定义了一个函数来生成输入输出数据，用于训练模型。然后，我们将数据转换为张量，并定义了一个 Transformer 模型。接着，我们定义了训练和测试函数，使用 Adam 优化器和均方误差损失函数进行模型训练。在训练完成后，我们使用模型对测试数据进行预测，并将预测结果反归一化，以得到真实的预测结果。最后，我们输出了预测结果和真实结果，以便进行比较和评估。