我正在尝试使用PyBrain进行时间序列预测，采用了这个解决方案。其他方法会产生较大的偏差。问题在于，尽管我尝试了调整学习率、动量、最大训练轮数、继续训练轮数、神经元数量（1-500）以及激活函数，结果始终是平坦的。可能的解决方案是什么？

蓝色：原始数据。绿色：网络预测。

INPUTS = 60HIDDEN = 60OUTPUTS = 1def build_network():    net = buildNetwork(INPUTS, HIDDEN, OUTPUTS,                       hiddenclass=LSTMLayer,                       outclass=LinearLayer,                       recurrent=True,                       bias=True, outputbias=False)    net.sortModules()    return netdef prepare_datasets(data, training_data_ratio):    training_data, validation_data = split_list(data, training_data_ratio)    training_set = SequentialDataSet(INPUTS, OUTPUTS)    for i in range(len(training_data) - INPUTS - 1):        training_set.newSequence()        tr_inputs = training_data[i:i + INPUTS]        tr_output = training_data[i + INPUTS]        training_set.addSample(tr_inputs, tr_output)    validation_set = []    for i in range(len(validation_data) - INPUTS - 1):        validation_set.append(validation_data[i:i + INPUTS])    return training_set, validation_setdef train_network(net, data, max_iterations):    net.randomize()    learning_rate = 0.1    trainer = BackpropTrainer(net, data, verbose=True,                              momentum=0.8,                              learningrate=learning_rate)    errors = trainer.trainUntilConvergence(maxEpochs=max_iterations, continueEpochs=10)    return errorsdef try_network(net, data):    outputs = []    for item in data:        output = net.activate(item)[0]        outputs.append(output)    return outputs

回答：

数据标准化：

data = data / max(data)