shamsulmasum commented 7 years ago

`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)`