Python时间序列LSTM预测系列学习笔记（11）-多步预测_python n_lag = n_seq = n_test = n_epochs = n_batch

本文是对：

https://machinelearningmastery.com/multi-step-time-series-forecasting-long-short-term-memory-networks-python/

https://blog.csdn.net/iyangdi/article/details/77895186

博文的学习笔记，博主笔风都很浪，有些细节一笔带过，本人以谦逊的态度进行了学习和整理，笔记内容都在代码的注释中。有不清楚的可以去原博主文中查看。

数据集下载：https://datamarket.com/data/set/22r0/sales-of-shampoo-over-a-three-year-period

后期我会补上我的github

源码地址：https://github.com/yangwohenmai/LSTM/tree/master/LSTM%E7%B3%BB%E5%88%97/Multi-Step%20LSTM%E9%A2%84%E6%B5%8B2

本文其实是iyangdi博主最后一篇LSTM的文章，后续没有继续进行连载，不过后面的课程我会继续通过对Jason Brownlee博士文章的学习上传上来

本文在上文的基础上，对真实数据进行了处理，进行了一次实战的多步预测

上一章节可能有人疑惑为什么数据预测出来都是横线，是因为那些数据都是没有意义的实验数据

本节中的数据是真实数据

代码分析写在了注释里

from pandas import DataFrame
from pandas import Series
from pandas import concat
from pandas import read_csv
from pandas import datetime
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from math import sqrt
from matplotlib import pyplot
from numpy import array
 
 
# 加载数据集
def parser(x):
    return datetime.strptime(x, '%Y/%m/%d')
 
 
# 将时间序列转换为监督类型的数据序列
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
    n_vars = 1 if type(data) is list else data.shape[1]
    df = DataFrame(data)
    cols, names = list(), list()
    # 这个for循环是用来输入列标题的 var1(t-1)，var1(t)，var1(t+1)，var1(t+2)
    for i in range(n_in, 0, -1):
        cols.append(df.shift(i))
        names += [('var%d(t-%d)' % (j + 1, i)) for j in range(n_vars)]
    # 转换为监督型数据的预测序列 每四个一组，对应 var1(t-1)，var1(t)，var1(t+1)，var1(t+2)
    for i in range(0, n_out):
        cols.append(df.shift(-i))
        if i == 0:
            names += [('var%d(t)' % (j + 1)) for j in range(n_vars)]
        else:
            names += [('var%d(t+%d)' % (j + 1, i)) for j in range(n_vars)]
    # 拼接数据
    agg = concat(cols, axis=1)
    agg.columns = names
    # 把null值转换为0
    if dropnan:
        agg.dropna(inplace=True)
    print(agg)
    return agg
 
 
# 对传入的数列做差分操作，相邻两值相减
def difference(dataset, interval=1):
    diff = list()
    for i in range(interval, len(dataset)):
        value = dataset[i] - dataset[i - interval]
        diff.append(value)
    return Series(diff)
 
 
# 将序列转换为用于监督学习的训练和测试集
def prepare_data(series, n_test, n_lag, n_seq):
    # 提取原始值
    raw_values = series.values
    # 将数据转换为静态的
    diff_series = difference(raw_values, 1)
    diff_values = diff_series.values
    diff_values = diff_values.reshape(len(diff_values), 1)
    # 重新调整数据为（-1,1）之间
    scaler = MinMaxScaler(feature_range=(-1, 1))
    scaled_values = scaler.fit_transform(diff_values)
    scaled_values = scaled_values.reshape(len(scaled_values), 1)
    # 转化为有监督的数据X，y
    supervised = series_to_supervised(scaled_values, n_lag, n_seq)
    supervised_values = supervised.values
    # 分割为测试数据和训练数据
    train, test = supervised_values[0:-n_test], supervised_values[-n_test:]
    return scaler, train, test
 
 
# 匹配LSTM网络训练数据
def fit_lstm(train, n_lag, n_seq, n_batch, nb_epoch, n_neurons):
    # 重塑训练数据格式 [samples, timesteps, features]
    X, y = train[:, 0:n_lag], train[:, n_lag:]
    X = X.reshape(X.shape[0], 1, X.shape[1])
    # 配置一个LSTM神经网络，添加网络参数
    model = Sequential()
    model.add(LSTM(n_neurons, batch_input_shape=(n_batch, X.shape[1], X.shape[2]), stateful=True))
    model.add(Dense(y.shape[1]))
    model.compile(loss='mean_squared_error', optimizer='adam')
    # 调用网络，迭代数据对神经网络进行训练，最后输出训练好的网络模型
    for i in range(nb_epoch):
        model.fit(X, y, epochs=1, batch_size=n_batch, verbose=0, shuffle=False)
        model.reset_states()
    return model
 
 
# 用LSTM做预测
def forecast_lstm(model, X, n_batch):
    # 重构输入参数 [samples, timesteps, features]
    X = X.reshape(1, 1, len(X))
    # 开始预测
    forecast = model.predict(X, batch_size=n_batch)
    # 结果转换成数组
    return [x for x in forecast[0, :]]
 
 
# 利用训练好的网络模型，对测试数据进行预测
def make_forecasts(model, n_batch, train, test, n_lag, n_seq):
    forecasts = list()
    # 预测方式是用一个X值预测出后三步的Y值
    for i in range(len(test)):
        X, y = test[i, 0:n_lag], test[i, n_lag:]
        # 调用训练好的模型预测未来数据
        forecast = forecast_lstm(model, X, n_batch)
        # 将预测的数据保存
        forecasts.append(forecast)
    return forecasts
 
 
# 对预测后的缩放值（-1，1）进行逆变换
def inverse_difference(last_ob, forecast):
    # invert first forecast
    inverted = list()
    inverted.append(forecast[0] + last_ob)
    # propagate difference forecast using inverted first value
    for i in range(1, len(forecast)):
        inverted.append(forecast[i] + inverted[i - 1])
    return inverted
 
 
# 对预测完成的数据进行逆变换
def inverse_transform(series, forecasts, scaler, n_test):
    inverted = list()
    for i in range(len(forecasts)):
        # create array from forecast
        forecast = array(forecasts[i])
        forecast = forecast.reshape(1, len(forecast))
        # 将预测后的数据缩放逆转换
        inv_scale = scaler.inverse_transform(forecast)
        inv_scale = inv_scale[0, :]
        # invert differencing
        index = len(series) - n_test + i - 1
        last_ob = series.values[index]
        # 将预测后的数据差值逆转换
        inv_diff = inverse_difference(last_ob, inv_scale)
        # 保存数据
        inverted.append(inv_diff)
    return inverted
 
 
# 评估每个预测时间步的RMSE
def evaluate_forecasts(test, forecasts, n_lag, n_seq):
    for i in range(n_seq):
        actual = [row[i] for row in test]
        predicted = [forecast[i] for forecast in forecasts]
        rmse = sqrt(mean_squared_error(actual, predicted))
        print('t+%d RMSE: %f' % ((i + 1), rmse))
 
 
# 在原始数据集的上下文中绘制预测图
def plot_forecasts(series, forecasts, n_test):
    # plot the entire dataset in blue
    pyplot.plot(series.values)
    # plot the forecasts in red
    for i in range(len(forecasts)):
        off_s = len(series) - n_test + i - 1
        off_e = off_s + len(forecasts[i]) + 1
        xaxis = [x for x in range(off_s, off_e)]
        yaxis = [series.values[off_s]] + forecasts[i]
        pyplot.plot(xaxis, yaxis, color='red')
    # show the plot
    pyplot.show()
 
 
# 加载数据
series = read_csv('data_set/shampoo-sales.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)
# 配置网络信息
n_lag = 1
n_seq = 3
n_test = 10
n_epochs = 1500
n_batch = 1
n_neurons = 1
# 准备数据
scaler, train, test = prepare_data(series, n_test, n_lag, n_seq)
# 准备预测模型
model = fit_lstm(train, n_lag, n_seq, n_batch, n_epochs, n_neurons)
# 开始预测
forecasts = make_forecasts(model, n_batch, train, test, n_lag, n_seq)
# 逆转换训练数据和预测数据
forecasts = inverse_transform(series, forecasts, scaler, n_test + 2)
# 逆转换测试数据
actual = [row[n_lag:] for row in test]
actual = inverse_transform(series, actual, scaler, n_test + 2)
# 比较预测数据和测试数据，计算两者之间的损失值
evaluate_forecasts(actual, forecasts, n_lag, n_seq)
# 画图
plot_forecasts(series, forecasts, n_test + 2)