A. 如何在Python中用LSTM网络进行时间序列预测
时间序列模型
时间序列预测分析就是利用过去一段时间内某事件时间的特征来预测未来一段时间内该事件的特征。这是一类相对比较复杂的预测建模问题，和回归分析模型的预测不同，时间序列模型是依赖于事件发生的先后顺序的，同样大小的值改变顺序后输入模型产生的结果是不同的。
举个栗子：根据过去两年某股票的每天的股价数据推测之后一周的股价变化；根据过去2年某店铺每周想消费人数预测下周来店消费的人数等等
RNN 和 LSTM 模型
时间序列模型最常用最强大的的工具就是递归神经网络（recurrent neural network, RNN）。相比与普通神经网络的各计算结果之间相互独立的特点，RNN的每一次隐含层的计算结果都与当前输入以及上一次的隐含层结果相关。通过这种方法，RNN的计算结果便具备了记忆之前几次结果的特点。
典型的RNN网路结构如下：
4. 模型训练和结果预测
将上述数据集按4:1的比例随机拆分为训练集和验证集，这是为了防止过度拟合。训练模型。然后将数据的X列作为参数导入模型便可得到预测值，与实际的Y值相比便可得到该模型的优劣。
实现代码
时间间隔序列格式化成所需的训练集格式
import pandas as pdimport numpy as npdef create_interval_dataset(dataset, look_back): """ :param dataset: input array of time intervals :param look_back: each training set feature length :return: convert an array of values into a dataset matrix. """ dataX, dataY = [], [] for i in range(len(dataset) - look_back): dataX.append(dataset[i:i+look_back]) dataY.append(dataset[i+look_back]) return np.asarray(dataX), np.asarray(dataY)df = pd.read_csv("path-to-your-time-interval-file") dataset_init = np.asarray(df) # if only 1 columndataX, dataY = create_interval_dataset(dataset, lookback=3) # look back if the training set sequence length这里的输入数据来源是csv文件，如果输入数据是来自数据库的话可以参考这里
LSTM网络结构搭建
import pandas as pdimport numpy as npimport randomfrom keras.models import Sequential, model_from_jsonfrom keras.layers import Dense, LSTM, Dropoutclass NeuralNetwork(): def __init__(self, **kwargs): """ :param **kwargs: output_dim=4: output dimension of LSTM layer; activation_lstm='tanh': activation function for LSTM layers; activation_dense='relu': activation function for Dense layer; activation_last='sigmoid': activation function for last layer; drop_out=0.2: fraction of input units to drop; np_epoch=10, the number of epoches to train the model. epoch is one forward pass and one backward pass of all the training examples; batch_size=32: number of samples per gradient update. The higher the batch size, the more memory space you'll need; loss='mean_square_error': loss function; optimizer='rmsprop' """ self.output_dim = kwargs.get('output_dim', 8) self.activation_lstm = kwargs.get('activation_lstm', 'relu') self.activation_dense = kwargs.get('activation_dense', 'relu') self.activation_last = kwargs.get('activation_last', 'softmax') # softmax for multiple output self.dense_layer = kwargs.get('dense_layer', 2) # at least 2 layers self.lstm_layer = kwargs.get('lstm_layer', 2) self.drop_out = kwargs.get('drop_out', 0.2) self.nb_epoch = kwargs.get('nb_epoch', 10) self.batch_size = kwargs.get('batch_size', 100) self.loss = kwargs.get('loss', 'categorical_crossentropy') self.optimizer = kwargs.get('optimizer', 'rmsprop') def NN_model(self, trainX, trainY, testX, testY): """ :param trainX: training data set :param trainY: expect value of training data :param testX: test data set :param testY: epect value of test data :return: model after training """ print "Training model is LSTM network!" input_dim = trainX[1].shape[1] output_dim = trainY.shape[1] # one-hot label # print predefined parameters of current model: model = Sequential() # applying a LSTM layer with x dim output and y dim input. Use dropout parameter to avoid overfitting model.add(LSTM(output_dim=self.output_dim, input_dim=input_dim, activation=self.activation_lstm, dropout_U=self.drop_out, return_sequences=True)) for i in range(self.lstm_layer-2): model.add(LSTM(output_dim=self.output_dim, input_dim=self.output_dim, activation=self.activation_lstm, dropout_U=self.drop_out, return_sequences=True)) # argument return_sequences should be false in last lstm layer to avoid input dimension incompatibility with dense layer model.add(LSTM(output_dim=self.output_dim, input_dim=self.output_dim, activation=self.activation_lstm, dropout_U=self.drop_out)) for i in range(self.dense_layer-1): model.add(Dense(output_dim=self.output_dim, activation=self.activation_last)) model.add(Dense(output_dim=output_dim, input_dim=self.output_dim, activation=self.activation_last)) # configure the learning process model.compile(loss=self.loss, optimizer=self.optimizer, metrics=['accuracy']) # train the model with fixed number of epoches model.fit(x=trainX, y=trainY, nb_epoch=self.nb_epoch, batch_size=self.batch_size, validation_data=(testX, testY)) # store model to json file model_json = model.to_json() with open(model_path, "w") as json_file: json_file.write(model_json) # store model weights to hdf5 file if model_weight_path: if os.path.exists(model_weight_path): os.remove(model_weight_path) model.save_weights(model_weight_path) # eg: model_weight.h5 return model这里写的只涉及LSTM网络的结构搭建，至于如何把数据处理规范化成网络所需的结构以及把模型预测结果与实际值比较统计的可视化，就需要根据实际情况做调整了。
