I just built this LSTM neural network with Keras
import numpy as np
import pandas as pd
from sklearn import preprocessing
from keras.layers.core import Dense, Dropout, Activation
from keras.activations import linear
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from matplotlib import pyplot
#read and prepare data from datafile
data_file_name = "DailyDemand.csv"
data_csv = pd.read_csv(data_file_name, delimiter = ';',header=None, usecols=[1,2,3,4,5])
yt = data_csv[1:]
data = yt
data.columns = ['MoyenneTransactHier', 'MaxTransaction', 'MinTransaction','CountTransaction','Demand']
# print (data.head(10))
pd.options.display.float_format = '{:,.0f}'.format
data = data.dropna ()
y=data['Demand'].astype(int)
cols=['MoyenneTransactHier', 'MaxTransaction', 'MinTransaction','CountTransaction']
x=data[cols].astype(int)
#scaling data
scaler_x = preprocessing.MinMaxScaler(feature_range =(-1, 1))
x = np.array(x).reshape ((len(x),4 ))
x = scaler_x.fit_transform(x)
scaler_y = preprocessing.MinMaxScaler(feature_range =(-1, 1))
y = np.array(y).reshape ((len(y), 1))
y = scaler_y.fit_transform(y)
print("longeur de y",len(y))
# Split train and test data
train_end = 80
x_train=x[0: train_end ,]
x_test=x[train_end +1: ,]
y_train=y[0: train_end]
y_test=y[train_end +1:]
x_train=x_train.reshape(x_train.shape +(1,))
x_test=x_test.reshape(x_test.shape + (1,))
print("Data well prepared")
print ('x_train shape ', x_train.shape)
print ('y_train', y_train.shape)
#Design the model - LSTM Network
seed = 2016
np.random.seed(seed)
fit1 = Sequential ()
fit1.add(LSTM(
output_dim = 4,
activation='tanh',
input_shape =(4, 1)))
fit1.add(Dense(output_dim =1))
fit1.add(Activation(linear))
#rmsprop or sgd
batchsize = 1
fit1.compile(loss="mean_squared_error",optimizer="rmsprop")
#train the model
fit1.fit(x_train , y_train , batch_size = batchsize, nb_epoch =20, shuffle=True)
print(fit1.summary ())
#Model error
score_train = fit1.evaluate(x_train ,y_train ,batch_size =batchsize)
score_test = fit1.evaluate(x_test , y_test ,batch_size =batchsize)
print("in train MSE = ",round(score_train,4))
print("in test MSE = ",round(score_test ,4))
#Make prediction
pred1=fit1.predict(x_test)
pred1 = scaler_y.inverse_transform(np.array(pred1).reshape ((len(pred1), 1)))
real_test = scaler_y.inverse_transform(np.array(y_test).reshape ((len(y_test), 1))).astype(int)
#save prediction
testData = pd.DataFrame(real_test)
preddData = pd.DataFrame(pred1)
dataF = pd.concat([testData,preddData], axis=1)
dataF.columns =['Real demand','Predicted Demand']
dataF.to_csv('Demandprediction.csv')
pyplot.plot(pred1, label='Forecast')
pyplot.plot(real_test,label='Actual')
pyplot.legend()
pyplot.show()
then it generates this result:
After building and training a good model on the historical data, I don't know how I can generate the prediction for future values? For example the demand of the next 10 days. Data are daily.
NB: this is an example of how the data is shaped, the green is the label and the yellow one are the features.
after dropna()
(delete null values) it remains 100 data rows, I've used 80 in the training and the 20 in the test.