时间序列的seq2seq预测 [英] seq2seq prediction for time series

问题描述

我想为重建目的制作一个Seq2Seq模型.我想要一个经过训练的模型来重建正常的时间序列，并且假设这样的模型在重建训练期间没有看到异常时间序列的过程中会表现得很差.

I want to make a Seq2Seq model for reconstruction purpose. I want a model trained to reconstruct the normal time-series and it is assumed that such a model would do badly to reconstruct the anomalous time-series having not seen them during training.

我的代码和理解上都有差距.我将其作为一种方向，并且到目前为止: traindata:input_data.shape(1000,60,1)和target_data.shape(1000,50,1)，目标数据是与训练数据相同的训练数据，但颠倒顺序与论文此处. 进行推断:我想使用形状为(3000,60,1)的经过训练的模型预测另一个时间序列数据. T现在有2点是开放的:如何为我的训练模型指定输入数据，以及如何使用停止条件构建推理部分? 请更正任何错误.

I have some gaps in my code and also in the understanding. I took this as an orientation and did so far: traindata: input_data.shape(1000,60,1) and target_data.shape(1000,50,1) with target data being the same training data only in reversed order as sugested in the paper here. for inference: I want to predict another time series data with the trained model having the shape (3000,60,1). T Now 2 points are open: how do I specify the input data for my training model and how do I build the inference part with the stop condition ? 
 Please correct any mistakes.

from keras.models import Model
from keras.layers import Input
from keras.layers import LSTM
from keras.layers import Dense

num_encoder_tokens = 1#number of features
num_decoder_tokens = 1#number of features
encoder_seq_length = None
decoder_seq_length = None
batch_size = 50
epochs = 40

# same data for training 
input_seqs=()#shape (1000,60,1) with sliding windows
target_seqs=()#shape(1000,60,1) with sliding windows but reversed
x= #what has x to be ?

#data for inference 
# how do I specify the input data for my other time series ?

# Define training model
encoder_inputs = Input(shape=(encoder_seq_length,
                          num_encoder_tokens))
encoder = LSTM(128, return_state=True, return_sequences=True)
encoder_outputs = encoder(encoder_inputs)
_, encoder_states = encoder_outputs[0], encoder_outputs[1:]

decoder_inputs = Input(shape=(decoder_seq_length,
                          num_decoder_tokens))
decoder = LSTM(128, return_sequences=True)
decoder_outputs = decoder(decoder_inputs, initial_state=encoder_states)
decoder_outputs = TimeDistributed(
Dense(num_decoder_tokens, activation='tanh'))(decoder_outputs)
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)

# Training
model.compile(optimizer='adam', loss='mse')
model.fit([input_seqs,x], target_seqs,
      batch_size=batch_size, epochs=epochs)


# Define sampling models for inference
encoder_model = Model(encoder_inputs, encoder_states)

decoder_state_input_h = Input(shape=(100,))
decoder_state_input_c = Input(shape=(100,))
decoder_states = [decoder_state_input_h, decoder_state_input_c]
decoder_outputs = decoder(decoder_inputs,
                      initial_state=decoder_states)
decoder_model = Model([decoder_inputs] + decoder_states,
decoder_outputs)

# Sampling loop for a batch of sequences
states_values = encoder_model.predict(input_seqs)
stop_condition = False
while not stop_condition:
    output_tokens = decoder_model.predict([target_seqs] + states_values)
#what else do I need to include here ? 
    break

推荐答案

def predict_sequence(infenc, infdec, source, n_steps, cardinality):
    # encode
    state = infenc.predict(source)
    # start of sequence input
    target_seq = array([0.0 for _ in range(cardinality)]).reshape(1, 1, cardinality)
    # collect predictions
    output = list()
    for t in range(n_steps):
        # predict next char
        yhat, h, c = infdec.predict([target_seq] + state)
        # store prediction
        output.append(yhat[0,0,:])
        # update state
        state = [h, c]
        # update target sequence
        target_seq = yhat
    return array(output)

您可以看到每个时间步的输出都从外部反馈到LSTM单元.

You can see that the output from every timestep is fed back to the LSTM cell externally.

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