Python时间序列LSTM预测系列教程（11）-多步预测

Multi-Step LSTM预测（2）

教程原文链接

关联教程：

Python时间序列LSTM预测系列教程（10）-多步预测

Python时间序列LSTM预测系列教程（11）-多步预测

多步预测的LSTM网络

数据准备

1、变成具有稳定性数据

2、缩放数据

Python时间序列LSTM预测系列教程（2）-单变量

LSTM模型预测过程

1、数据预测处理，准备数据

2、定义模型

3、训练模型

4、预测

5、数据逆变换

6、评估

代码

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

# date-time parsing function for loading the dataset
def parser(x):
return datetime.strptime( '190'+x, '%Y-%m')

# convert time series into supervised learning problem
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()
# input sequence (t-n, ... t-1)
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)]
# forecast sequence (t, t+1, ... t+n)
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)]
# put it all together
agg = concat(cols, axis= 1)
agg.columns = names
# drop rows with NaN values
if dropnan:
agg.dropna(inplace= True)
return agg

# create a differenced series
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)

# transform series into train and test sets for supervised learning
def prepare_data(series, n_test, n_lag, n_seq):
# extract raw values
raw_values = series.values
# transform data to be stationary
diff_series = difference(raw_values, 1)
diff_values = diff_series.values
diff_values = diff_values.reshape(len(diff_values), 1)
# rescale values to -1, 1
scaler = MinMaxScaler(feature_range=( -1, 1))
scaled_values = scaler.fit_transform(diff_values)
scaled_values = scaled_values.reshape(len(scaled_values), 1)
# transform into supervised learning problem X, y
supervised = series_to_supervised(scaled_values, n_lag, n_seq)
supervised_values = supervised.values
# split into train and test sets
train, test = supervised_values[ 0:-n_test], supervised_values[-n_test:]
return scaler, train, test

# fit an LSTM network to training data
def fit_lstm(train, n_lag, n_seq, n_batch, nb_epoch, n_neurons):
# reshape training into [samples, timesteps, features]
X, y = train[:, 0:n_lag], train[:, n_lag:]
X = X.reshape(X.shape[ 0], 1, X.shape[ 1])
# design network
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')
# fit network
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

# make one forecast with an LSTM,
def forecast_lstm(model, X, n_batch):
# reshape input pattern to [samples, timesteps, features]
X = X.reshape( 1, 1, len(X))
# make forecast
forecast = model.predict(X, batch_size=n_batch)
# convert to array
return [x for x in forecast[ 0, :]]

# evaluate the persistence model
def make_forecasts(model, n_batch, train, test, n_lag, n_seq):
forecasts = list()
for i in range(len(test)):
X, y = test[i, 0:n_lag], test[i, n_lag:]
# make forecast
forecast = forecast_lstm(model, X, n_batch)
# store the forecast
forecasts.append(forecast)
return forecasts

# invert differenced forecast
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

# inverse data transform on forecasts
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))
# invert scaling
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]
inv_diff = inverse_difference(last_ob, inv_scale)
# store
inverted.append(inv_diff)
return inverted

# evaluate the RMSE for each forecast time step
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))

# plot the forecasts in the context of the original dataset
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()

# load dataset
series = read_csv( 'shampoo-sales.csv', header= 0, parse_dates=[ 0], index_col= 0, squeeze= True, date_parser=parser)
# configure
n_lag = 1
n_seq = 3
n_test = 10
n_epochs = 1500
n_batch = 1
n_neurons = 1
# prepare data
scaler, train, test = prepare_data(series, n_test, n_lag, n_seq)
# fit model
model = fit_lstm(train, n_lag, n_seq, n_batch, n_epochs, n_neurons)
# make forecasts
forecasts = make_forecasts(model, n_batch, train, test, n_lag, n_seq)
# inverse transform forecasts and test
forecasts = inverse_transform(series, forecasts, scaler, n_test+ 2)
actual = [row[n_lag:] for row in test]
actual = inverse_transform(series, actual, scaler, n_test+ 2)
# evaluate forecasts
evaluate_forecasts(actual, forecasts, n_lag, n_seq)
# plot forecasts
plot_forecasts(series, forecasts, n_test+ 2)