使用tensorflow的lstm网络进行时间序列预测_基于tensorflow引擎创建的预测算法 预测未来 半年 数据

这篇文章将讲解如何使用lstm进行时间序列方面的预测，重点讲lstm的应用，原理部分可参考以下两篇文章：

Understanding LSTM Networks       LSTM学习笔记

编程环境：python3.5，tensorflow 1.0

本文所用的数据集来自于kesci平台，由云脑机器学习实战训练营提供：真实业界数据的时间序列预测挑战

数据集采用来自业界多组相关时间序列（约40组）与外部特征时间序列（约5组）。本文只使用其中一组数据进行建模。

加载常用的库：

#加载数据分析常用库
import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.metrics import mean_absolute_error,mean_squared_error
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
% matplotlib inline
import warnings 
warnings.filterwarnings('ignore')

path = '../input/industry/industry_timeseries/timeseries_train_data/11.csv'
data11 = pd.read_csv(path,names=['年','月','日','当日最高气温','当日最低气温','当日平均气温','当日平均湿度','输出'])
data11.head()

##load data(本文以第一个表为例，其他表类似，不再赘述)
f=open('../input/industry/industry_timeseries/timeseries_train_data/11.csv') 
df=pd.read_csv(f)     #读入数据
data=df.iloc[:,3:8].values   #取第3-7列

#定义常量
rnn_unit=10       #hidden layer units
input_size=4      
output_size=1
lr=0.0006         #学习率
tf.reset_default_graph()
#输入层、输出层权重、偏置
weights={
         'in':tf.Variable(tf.random_normal([input_size,rnn_unit])),
         'out':tf.Variable(tf.random_normal([rnn_unit,1]))
         }
biases={
        'in':tf.Variable(tf.constant(0.1,shape=[rnn_unit,])),
        'out':tf.Variable(tf.constant(0.1,shape=[1,]))
        }

def get_data(batch_size=60,time_step=20,train_begin=0,train_end=487):
    batch_index=[]
        
    scaler_for_x=MinMaxScaler(feature_range=(0,1))  #按列做minmax缩放
    scaler_for_y=MinMaxScaler(feature_range=(0,1))
    scaled_x_data=scaler_for_x.fit_transform(data[:,:-1])
    scaled_y_data=scaler_for_y.fit_transform(data[:,-1])
    
    label_train = scaled_y_data[train_begin:train_end]
    label_test = scaled_y_data[train_end:]
    normalized_train_data = scaled_x_data[train_begin:train_end]
    normalized_test_data = scaled_x_data[train_end:]
    
    train_x,train_y=[],[]   #训练集x和y初定义
    for i in range(len(normalized_train_data)-time_step):
        if i % batch_size==0:
            batch_index.append(i)
        x=normalized_train_data[i:i+time_step,:4]
        y=label_train[i:i+time_step,np.newaxis]
        train_x.append(x.tolist())
        train_y.append(y.tolist())
    batch_index.append((len(normalized_train_data)-time_step))
    
    size=(len(normalized_test_data)+time_step-1)//time_step  #有size个sample 
    test_x,test_y=[],[]  
    for i in range(size-1):
        x=normalized_test_data[i*time_step:(i+1)*time_step,:4]
        y=label_test[i*time_step:(i+1)*time_step]
        test_x.append(x.tolist())
        test_y.extend(y)
    test_x.append((normalized_test_data[(i+1)*time_step:,:4]).tolist())
    test_y.extend((label_test[(i+1)*time_step:]).tolist())    
    
    return batch_index,train_x,train_y,test_x,test_y,scaler_for_y

#——————————————————定义神经网络变量——————————————————
def lstm(X):  
    batch_size=tf.shape(X)[0]
    time_step=tf.shape(X)[1]
    w_in=weights['in']
    b_in=biases['in']  
    input=tf.reshape(X,[-1,input_size])  #需要将tensor转成2维进行计算，计算后的结果作为隐藏层的输入
    input_rnn=tf.matmul(input,w_in)+b_in
    input_rnn=tf.reshape(input_rnn,[-1,time_step,rnn_unit])  #将tensor转成3维，作为lstm cell的输入
    cell=tf.contrib.rnn.BasicLSTMCell(rnn_unit)
    #cell=tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(rnn_unit)
    init_state=cell.zero_state(batch_size,dtype=tf.float32)
    output_rnn,final_states=tf.nn.dynamic_rnn(cell, input_rnn,initial_state=init_state, dtype=tf.float32)  #output_rnn是记录lstm每个输出节点的结果，final_states是最后一个cell的结果
    output=tf.reshape(output_rnn,[-1,rnn_unit]) #作为输出层的输入
    w_out=weights['out']
    b_out=biases['out']
    pred=tf.matmul(output,w_out)+b_out
    return pred,final_states

#——————————————————训练模型——————————————————
def train_lstm(batch_size=80,time_step=15,train_begin=0,train_end=487):
    X=tf.placeholder(tf.float32, shape=[None,time_step,input_size])
    Y=tf.placeholder(tf.float32, shape=[None,time_step,output_size])
    batch_index,train_x,train_y,test_x,test_y,scaler_for_y = get_data(batch_size,time_step,train_begin,train_end)
    pred,_=lstm(X)
    #损失函数
    loss=tf.reduce_mean(tf.square(tf.reshape(pred,[-1])-tf.reshape(Y, [-1])))
    train_op=tf.train.AdamOptimizer(lr).minimize(loss)  
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        #重复训练5000次
        iter_time = 5000
        for i in range(iter_time):
            for step in range(len(batch_index)-1):
                _,loss_=sess.run([train_op,loss],feed_dict={X:train_x[batch_index[step]:batch_index[step+1]],Y:train_y[batch_index[step]:batch_index[step+1]]})
            if i % 100 == 0:    
                print('iter:',i,'loss:',loss_)
        ####predict####
        test_predict=[]
        for step in range(len(test_x)):
            prob=sess.run(pred,feed_dict={X:[test_x[step]]})   
            predict=prob.reshape((-1))
            test_predict.extend(predict)
            
        test_predict = scaler_for_y.inverse_transform(test_predict)
        test_y = scaler_for_y.inverse_transform(test_y)
        rmse=np.sqrt(mean_squared_error(test_predict,test_y))
        mae = mean_absolute_error(y_pred=test_predict,y_true=test_y)
        print ('mae:',mae,'   rmse:',rmse)
    return test_predict

test_predict = train_lstm(batch_size=80,time_step=15,train_begin=0,train_end=487)

iter: 3900 loss: 0.000505382
iter: 4000 loss: 0.000502154
iter: 4100 loss: 0.000503413
iter: 4200 loss: 0.00140424
iter: 4300 loss: 0.000500015
iter: 4400 loss: 0.00050004
iter: 4500 loss: 0.000498159
iter: 4600 loss: 0.000500861
iter: 4700 loss: 0.000519379
iter: 4800 loss: 0.000499999
iter: 4900 loss: 0.000501265
mae: 121.183626208    rmse: 162.049017904

plt.figure(figsize=(24,8))
plt.plot(data[:, -1])
plt.plot([None for _ in range(487)] + [x for x in test_predict])
plt.show()